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REVIEW 4 major objections 6 minor 3 cited by

ROVER: A Multi-Season Dataset for Visual SLAM

T0 review · 4 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read ROVER, a millimeter-accurate multi-season dataset of 39 park-and-garden recordings, shows that most visual SLAM systems degrade sharply in low light and dense vegetation, especially in summer and autumn.

desk verdict A genuinely useful dataset for visual SLAM in gardens and parks, with a benchmark whose exact numbers are undercut by unvalidated total-station ground truth; still deserves peer review. read the letter →

arxiv 2412.02506 v3 pith:TV4LQRFG submitted 2024-12-03 cs.RO cs.CV

Fabian Schmidt , Julian Daubermann , Marcel Mitschke , Constantin Blessing , Stefan Meyer , Markus Enzweiler , Abhinav Valada This is my paper · ORCID
classification cs.ROcs.CV
keywords visualSLAMmulti-seasondatasetbenchmarkoutdoorroboticsparkandgardenenvironmentslow-lightnavigationvisual-inertialodometrygroundtruthtrajectory
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper introduces ROVER, a benchmark dataset for visual SLAM (simultaneous localization and mapping with cameras) recorded in five outdoor campus, park, and garden locations across all four seasons and several lighting conditions, from daylight to night with artificial light. The authors claim the dataset is millimeter-accurate because robot positions come from a survey instrument tracking a reflector on the platform, and they use it to evaluate seven established visual and visual-inertial SLAM systems. Their central result is that most of these systems degrade sharply in low light and in dense vegetation, with the worst trajectory errors occurring in summer and autumn. If the benchmark is trustworthy, it provides a reusable testbed for long-term outdoor localization and shows that current visual SLAM methods still do not generalize well to semi-structured natural environments.

What carries the argument

The carrying mechanism is the recording platform and its ground-truth chain: a small lawnmower-like robot with monocular, stereo fisheye, and RGBD cameras plus internal and external inertial sensors, time-synchronized via satellite-disciplined network time, with three-dimensional position ground truth provided by a robotic total station (a survey instrument that tracks a reflector to millimeter accuracy) at 2-5 Hz and transformed to each sensor through CAD-based extrinsics. The benchmark side is carried by a standardized evaluation protocol: trajectories are filtered for coverage and temporal density, aligned with a least-squares transformation (similarity transform for monocular, rigid transform for scale-observable configurations), and scored by root-mean-square absolute trajectory error, relative pose error per meter, and success rate averaged over five trials per sequence. This combination lets the authors attribute performance differences to season and lighting rather than to sensor setup.

What would settle it

An independent check would be to re-record a handful of ROVER routes with a second positioning system of equal or better precision and compare its trajectory to the released ground truth; if the disagreement reaches the same size as the accuracy gaps between top-ranked SLAM configurations, the rankings would not be supported.

Watch

Extended reading notes

Core claim

The paper's central claim is that ROVER is a millimeter-accurate, multi-season visual SLAM benchmark covering 39 recordings, 7.2 km of trajectory, and roughly 1 TB of sensor data, and that the accompanying evaluation demonstrates a clear environmental robustness gap: stereo-inertial and RGBD configurations perform well in favorable lighting and moderate vegetation, but most SLAM systems produce large absolute trajectory errors and lower success rates in low-light and high-vegetation conditions, particularly in summer and autumn. The benchmark also finds that adding inertial data to RGBD configurations does not help and can hurt, and that monocular systems suffer from scale ambiguity in larger open areas. The authors present the dataset as addressing a gap left by existing agricultural, forest, and urban multi-season datasets, which rarely combine monocular, stereo, and RGBD sensing with year-round park-and-garden recordings.

Load-bearing premise

The whole benchmark ranking depends on the assumption that the ground-truth path, measured by a survey instrument tracking a reflector and then shifted to each camera's position, stays accurate enough after being interpolated to camera times to support error differences that are sometimes only a few centimeters apart.

Editorial extensions

If this is right

  • If ROVER is millimeter-accurate, it can serve as a common testbed for comparing visual SLAM systems in semi-structured outdoor environments, complementing indoor and urban benchmarks.
  • The finding that most systems degrade at night and in summer or autumn implies that robustness to low light and dense vegetation should be a first-class evaluation criterion for outdoor SLAM.
  • The result that stereo-inertial and RGBD configurations outperform monocular ones in these environments supports prioritizing depth-capable or inertial-fused setups for park-and-garden robots.
  • The observation that RGBD-inertial performs worse than RGBD-only suggests that adding IMU data is not a guaranteed improvement in this setting.
  • The repeated overlapping rounds of the Perimeter recording scenario enable multi-session mapping and loop-closure evaluation across seasons.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper: the reported seasonal degradation suggests a concrete stress test for future SLAM systems, namely scoring separately on the summer and autumn subset, since averaging across seasons can hide failure modes.
  • Beyond the paper: before using ROVER to rank algorithms, a user should verify the claimed millimeter accuracy on a few sequences with an independent reference, because the accuracy gaps between top systems are small enough that unvalidated ground-truth interpolation could influence the ordering.
  • Beyond the paper: the dataset's repeated routes across seasons could be repurposed for place recognition and re-localization experiments, not only trajectory evaluation.
  • Beyond the paper: if the lighting results generalize, a testable prediction is that active illumination plus exposure-adaptive preprocessing will improve feature-based SLAM at night more than adding more inertial sensors.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. ROVER is a multi-season visual SLAM dataset collected with a small UGV at five park and garden locations, providing 39 recordings over approximately 7.2 km and 311 minutes with monocular (PiCam), stereo/RGBD (RealSense D435i, T265), and inertial (internal and VN100) data. The paper describes the robotic platform, sensor calibration, time synchronization, dataset organization, and a benchmark of seven SLAM algorithms across supported sensor configurations, reporting ATE, RPE, and success rate after five trials per sequence. The main empirical claims are that RGBD and stereo-inertial configurations generally outperform monocular ones and that most systems degrade substantially in summer and autumn and under low-light conditions.

Significance. If the ground-truth trajectories are as accurate as claimed, ROVER would be a valuable complement to existing outdoor SLAM datasets: it combines multi-season coverage, multiple camera modalities, consistent perimeter scenarios across five semi-structured locations, and a public release of raw data with ROS conversion tooling. The benchmark also has a reproducible core, using open-source SLAM implementations with pretrained weights, Umeyama alignment, and the evo toolbox for ATE/RPE. The main scientific value, however, depends on the end-to-end accuracy of the total-station reference and on the statistical support for the reported performance orderings, both of which need strengthening.

major comments (4)
  1. [III-B3, III-B4, III-C2, V-B] The 'millimeter-accurate' claim is based on the Leica TS16 sensor specification, but the end-to-end reference trajectory is not validated. The TS16 tracks a prism at 2-5 Hz, and the released groundtruth.txt is obtained by linear interpolation to camera timestamps and by CAD-model extrinsics from the prism to each sensor. At 0.5 m/s, consecutive fixes are 0.1-0.25 m apart, so interpolation during turns or on uneven terrain can easily produce decimeter-level errors. The reported mATE values in Tables VII-XII are about 0.3-1.5 m for the best configurations, and several inter-method gaps (e.g., ORB-SLAM3 RGBD 1.26 m vs. DROID-SLAM 2.32 m in Table X; OpenVINS 1.43 m vs. SVO Pro 1.70 m in Table IX) are of the same order as the plausible ground-truth error. The paper should provide an independent validation of the reference (e.g., static repeatability tests, loop-closure residuals on the repeated perimeter rounds, or comparison with a higher-rate reference after careful alignment), or explicitly report per-trajectory ground-truth uncertainty and temper the 'millimeter-accurate' wording.
  2. [V-B] Each sequence is run five times, but the paper reports only aggregated mATE/mRPE and success rate. Because the trajectory filter removes failed runs, the reported means are computed over a varying, selection-dependent subset of trials, and no spread (standard deviation, median, or per-trial values) is given. Without this information, it is impossible to tell whether the seasonal and lighting differences in Tables XI and XII exceed run-to-run variability. Please report per-sequence and per-trial results, or at least medians with interquartile ranges and the number of valid runs underlying each table cell.
  3. [V-B] The validity criteria (at least 80% temporal coverage and at least one pose per second) are arbitrary and do not include any accuracy or divergence condition, so a trajectory that drifts badly for up to 20% of the run is still counted as successful. The success-rate metric is therefore a coarse proxy for robustness, and the reported SR values should be accompanied by a sensitivity analysis or a justification of these thresholds.
  4. [III-B3, V-B] The ground-truth reference contains only 3-DoF prism positions and no orientation, so the reported ATE/RPE metrics cannot evaluate rotational error. The paper does not state the pose relation used with evo (e.g., translation-only), so readers cannot know what the mRPE values include. Please specify this explicitly and discuss the impact on the comparison.
minor comments (6)
  1. [Table V] Table V lists '/t265/image_left' twice; the second entry should presumably be '/t265/image_right'. The IMU rates also appear inconsistent with Table II (D435i listed as 100/200 Hz in Table II but 300 Hz in Table V; T265 as 65/200 Hz but 265 Hz in Table V).
  2. [Table VII] Several mRPE entries in Table VII use comma decimal separators (4,24; 1,73; 2,15) while the rest of the table uses decimal points; please make the formatting uniform.
  3. [Abstract and Introduction] The contributions state 'multi-weather' coverage, but Table IV records only windy/sunny/cloudy/dusk/night/light conditions and no rain, snow, or fog; either add such sequences or soften the 'multi-weather' claim.
  4. [Figure 9] The qualitative trajectory plots in Figure 9 have no axis labels or scale bars, making it difficult to judge the reported drift magnitudes; please add them.
  5. [V-A] The sentence 'Following Zhang and Scaramuzza [70], SLAM methods rely solely on monocular systems, the trajectories are scaled...' is ungrammatical and should be rewritten.
  6. [Table I] The header of Table I is difficult to parse and the meaning of the x marks is not fully defined; please add a legend or restructure the columns so that the comparison is transparent.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: the benchmark evaluations are grounded in external SLAM implementations and independent total-station ground truth.

full rationale

The paper is a dataset and benchmark contribution. Its central deliverable is a recorded multi-season dataset with ground-truth trajectories from a Leica TS16 total station, and its benchmark evaluates independent, open-source SLAM implementations against that external reference. There is no fitted parameter that is later renamed as a prediction: the SLAM systems are pre-existing methods with pretrained weights or standard configurations, and the evaluation metrics (ATE, RPE, SR) are computed by the external Evo toolbox following the established methods of Sturm et al. and Zhang and Scaramuzza. No equation in the paper defines a claimed result in terms of the data it is supposed to predict. The cited prior works by co-authors (e.g., CL-SLAM and COVIO) appear only as related methods, not as load-bearing justification for the dataset's accuracy or for the benchmark conclusions. The only substantive concern—whether the 2–5 Hz total-station prism fixes, after interpolation and CAD-based extrinsics, are accurate enough to support the reported ranking—is a correctness/validation risk about ground-truth quality, not a circularity. It does not involve the paper deriving its conclusions from its own inputs, because the ground truth is an independent measurement source rather than an output of the algorithms being evaluated.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The central benchmark results rest on a small set of methodological choices: trajectory validity thresholds and the accuracy of the ground-truth and synchronization pipeline. No new physical entities or fitted parameters are introduced. The paper's own conclusions are not circular, but the unvalidated ground-truth assumptions are load-bearing.

free parameters (2)
  • Trajectory validity coverage threshold = 80% of scenario duration
    Section V-B defines a valid trajectory as covering at least 80% of the scenario duration. This threshold directly defines the success rate metric and influences all reported SR values.
  • Minimum pose rate for valid trajectory = 1 pose per second
    Section V-B requires at least one pose estimate per second for a trajectory to be considered valid. This filter affects which runs are counted as successful and therefore shapes the benchmark conclusions.
assumptions (4)
  • domain assumption Leica TS16 total station prism positions are accurate ground truth for the robot trajectory.
    Sections III-B3 and IV-C use the total station as the sole reference. Its accuracy is quoted from the specification, but the paper does not validate the position accuracy against an independent measurement system.
  • domain assumption GNSS-disciplined NTP provides millisecond-level time synchronization across all sensors.
    Section III-C2 states that all devices share a common time base via GNSS and NTP. If synchronization errors are larger than claimed, visual-inertial fusion and trajectory alignment results would be affected.
  • domain assumption CAD-based extrinsic calibration between sensors and the ground-truth prism is correct.
    Section III-B4 describes a manual CAD-based method for aligning sensors with the prism. No empirical verification of these transforms is reported, and the transforms are needed to transfer total-station positions to camera trajectories.
  • standard math Umeyama alignment and SIM(3)/SE(3) trajectory fitting are appropriate for evaluating the SLAM trajectories.
    Section V-B follows standard practice from Sturm et al. and Zhang and Scaramuzza, so this is a reasonable methodological axiom rather than an ad hoc assumption.

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Cite this review

Pith. "Pith review of ROVER: A Multi-Season Dataset for Visual SLAM." pith.science (2026). https://pith.science/paper/TV4LQRFG

@misc{pith2026241202506,
  author       = {Pith},
  title        = {Pith review of: ROVER: A Multi-Season Dataset for Visual SLAM},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TV4LQRFG}},
  note         = {Machine review of arXiv:2412.02506}
}
read the original abstract

Robust SLAM is a crucial enabler for autonomous navigation in natural, semi-structured environments such as parks and gardens. However, these environments present unique challenges for SLAM due to frequent seasonal changes, varying light conditions, and dense vegetation. These factors often degrade the performance of visual SLAM algorithms originally developed for structured urban environments. To address this gap, we present ROVER, a comprehensive benchmark dataset tailored for evaluating visual SLAM algorithms under diverse environmental conditions and spatial configurations. We captured the dataset with a robotic platform equipped with monocular, stereo, and RGBD cameras, as well as inertial sensors. It covers 39 recordings across five outdoor locations, collected through all seasons and various lighting scenarios, i.e., day, dusk, and night with and without external lighting. With this novel dataset, we evaluate several traditional and deep learning-based SLAM methods and study their performance in diverse challenging conditions. The results demonstrate that while stereo-inertial and RGBD configurations generally perform better under favorable lighting and moderate vegetation, most SLAM systems perform poorly in low-light and high-vegetation scenarios, particularly during summer and autumn. Our analysis highlights the need for improved adaptability in visual SLAM algorithms for outdoor applications, as current systems struggle with dynamic environmental factors affecting scale, feature extraction, and trajectory consistency. This dataset provides a solid foundation for advancing visual SLAM research in real-world, semi-structured environments, fostering the development of more resilient SLAM systems for long-term outdoor localization and mapping. The dataset and the code of the benchmark are available under https://iis-esslingen.github.io/rover.

Figures

Figures reproduced from arXiv: 2412.02506 by the authors.

Figure 1
Figure 1. Illustration of diverse outdoor environments captured across various [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Overview of the Robotic Platform. The left section shows the robotic platform, including specific sensor coordinate systems. The center [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Camera Perspectives. Example images showcasing the perspectives from each camera on the robotic platform. (a) The D435i captures RGB [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: The system architecture for data recording and synchronization. The diagram illustrates the setup for sensor data collection, processing, and [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: Overview of data recording locations. (a) Park: Natural area [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: Preview of our dataset capturing diverse environmental conditions and seasonal variations across (a) Summer, (b) Autumn, (c) Winter, and (d) [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]
Figure 7
Figure 7. Figure 7: Folder structure of the ROVER dataset. V. VISUAL SLAM BENCHMARK Evaluating SLAM algorithms across diverse conditions is essential to understanding their robustness and adaptability in real-world applications. This section presents a benchmark framework for assessing va…
Figure 8
Figure 8. Figure 8: Comparison of best-performing SLAM methods regarding mATE and mRPE under different lighting conditions and seasons. [PITH_FULL_IMAGE:figures/full_fig_p014_8.png]
Figure 9
Figure 9. Figure 9: Qualitative trajectory comparisons across environments. (a) Park, (b) Campus Large, (c) Garden Large, (d) Campus Small, and (e) Garden [PITH_FULL_IMAGE:figures/full_fig_p015_9.png]

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Reference graph

Works this paper leans on

74 extracted references · 53 canonical work pages · cited by 3 Pith papers

  1. [1]

    Recent developments and applications of simultaneous localization and mapping in agriculture,

    H. Ding, B. Zhang, J. Zhou, Y . Yan, G. Tian, and B. Gu, “Recent developments and applications of simultaneous localization and mapping in agriculture,” Journal of field robotics , vol. 39, no. 6, pp. 956–983, 2022

    work page 2022

  2. [2]

    A comprehensive survey of visual slam algorithms,

    A. Macario Barros, M. Michel, Y . Moline, G. Corre, and F. Carrel, “A comprehensive survey of visual slam algorithms,” Robotics, vol. 11, no. 1, p. 24, 2022. IEEE TRANSACTIONS ON ROBOTICS - SPECIAL COLLECTION: VISUAL SLAM. PREPRINT VERSION. 17

    work page 2022

  3. [3]

    The euroc micro aerial vehicle datasets,

    M. Burri, J. Nikolic, P. Gohl, T. Schneider, J. Rehder, S. Omari, M. W. Achtelik, and R. Siegwart, “The euroc micro aerial vehicle datasets,” The International Journal of Robotics Research , vol. 35, no. 10, pp. 1157–1163, 2016

    2016

  4. [4]

    Are we ready for autonomous driving? the kitti vision benchmark suite,

    A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for autonomous driving? the kitti vision benchmark suite,” in 2012 IEEE conference on computer vision and pattern recognition . IEEE, 2012, pp. 3354–3361

    2012

  5. [5]

    Dynamic object removal and spatio- temporal rgb-d inpainting via geometry-aware adversarial learning,

    B. Be ˇsi´c and A. Valada, “Dynamic object removal and spatio- temporal rgb-d inpainting via geometry-aware adversarial learning,” IEEE Transactions on Intelligent Vehicles, vol. 7, no. 2, pp. 170–185, 2022

    work page 2022

  6. [6]

    Under-canopy dataset for advancing simultaneous localization and mapping in agricultural robotics,

    J. Cuaran, A. E. Baquero Velasquez, M. Valverde Gasparino, N. K. Uppalapati, A. N. Sivakumar, J. Wasserman, M. Huzaifa, S. Adve, and G. Chowdhary, “Under-canopy dataset for advancing simultaneous localization and mapping in agricultural robotics,” The International Journal of Robotics Research , vol. 43, no. 6, pp. 739– 749, 2023

    work page 2023

  7. [7]

    An event-based vision dataset for visual navigation tasks in agricultural environments,

    A. Zujevs, M. Pudzs, V . Osadcuks, A. Ardavs, M. Galauskis, and J. Grundspenkis, “An event-based vision dataset for visual navigation tasks in agricultural environments,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) , 2021, pp. 13 769– 13 775

    work page 2021

  8. [8]

    Ard-vo: Agricultural robot data set of vineyards and olive groves,

    F. Crocetti, E. Bellocchio, A. Dionigi, S. Felicioni, G. Costante, M. L. Fravolini, and P. Valigi, “Ard-vo: Agricultural robot data set of vineyards and olive groves,” Journal of Field Robotics , vol. 40, no. 6, pp. 1678–1696, 2023

    work page 2023

Show all 74 references
  1. [9]

    Collection and evaluation of a long-term 4d agri-robotic dataset,

    R. Polvara, S. M. Mellado, I. Hroob, G. Cielniak, and M. Hanheide, “Collection and evaluation of a long-term 4d agri-robotic dataset,” arXiv preprint arXiv:2211.14013 , 2022

    2022 arXiv

  2. [10]

    Magro dataset: A dataset for simultaneous localization and mapping in agricultural environments,

    M. Marzoa Tanco, G. Trinidad Barnech, F. Andrade, J. Baliosian, M. LLofriu, J. Di Martino, and G. Tejera, “Magro dataset: A dataset for simultaneous localization and mapping in agricultural environments,” The International Journal of Robotics Research , vol. 43, no. 5, pp. 591...

    2024

  3. [11]

    Bacchus long-term (blt) data set: Acquisition of the agricultural multimodal blt data set with automated robot deployment,

    R. Polvara, S. Molina, I. Hroob, A. Papadimitriou, K. Tsiolis, D. Giakoumis, S. Likothanassis, D. Tzovaras, G. Cielniak, and M. Hanheide, “Bacchus long-term (blt) data set: Acquisition of the agricultural multimodal blt data set with automated robot deployment,” Journal of Fie...

    2023

  4. [12]

    Fomo: A proposal for a multi-season dataset for robot navigation in for ˆet montmorency,

    M. Boxan, A. Krawciw, E. Daum, X. Qiao, S. Lilge, T. D. Barfoot, and F. Pomerleau, “Fomo: A proposal for a multi-season dataset for robot navigation in for ˆet montmorency,” arXiv preprint arXiv:2404.13166, 2024

    2024 arXiv

  5. [13]

    Selective memory: recalling relevant experience for long-term visual localization,

    K. MacTavish, M. Paton, and T. D. Barfoot, “Selective memory: recalling relevant experience for long-term visual localization,” Journal of Field Robotics , vol. 35, no. 8, pp. 1265–1292, 2018

    2018

  6. [14]

    Visual slam: What are the current trends and what to expect?

    A. Tourani, H. Bavle, J. L. Sanchez-Lopez, and H. V oos, “Visual slam: What are the current trends and what to expect?” Sensors, vol. 22, no. 23, p. 9297, 2022

    2022

  7. [15]

    Deep learning techniques for visual slam: A survey,

    S. Mokssit, D. B. Licea, B. Guermah, and M. Ghogho, “Deep learning techniques for visual slam: A survey,” IEEE Access, vol. 11, pp. 20 026–20 050, 2023

    2023

  8. [16]

    How nerfs and 3d gaussian splatting are reshaping slam: a survey,

    F. Tosi, Y . Zhang, Z. Gong, E. Sandstr ¨om, S. Mattoccia, M. R. Oswald, and M. Poggi, “How nerfs and 3d gaussian splatting are reshaping slam: a survey,” arXiv preprint arXiv:2402.13255 , vol. 4, 2024

    2024 arXiv

  9. [17]

    1 year, 1000 km: The oxford robotcar dataset,

    W. Maddern, G. Pascoe, C. Linegar, and P. Newman, “1 year, 1000 km: The oxford robotcar dataset,” The International Journal of Robotics Research, vol. 36, no. 1, pp. 3–15, 2017

    2017

  10. [18]

    A cross-season correspondence dataset for robust semantic segmentation,

    M. Larsson, E. Stenborg, L. Hammarstrand, M. Pollefeys, T. Sattler, and F. Kahl, “A cross-season correspondence dataset for robust semantic segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 9532–9542

    2019

  11. [19]

    Eu long-term dataset with multiple sensors for autonomous driving,

    Z. Yan, L. Sun, T. Krajn ´ık, and Y . Ruichek, “Eu long-term dataset with multiple sensors for autonomous driving,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2020, pp. 10 697–10 704

    2020

  12. [20]

    4seasons: A cross-season dataset for multi-weather slam in autonomous driving,

    P. Wenzel, R. Wang, N. Yang, Q. Cheng, Q. Khan, L. von Stumberg, N. Zeller, and D. Cremers, “4seasons: A cross-season dataset for multi-weather slam in autonomous driving,” in Pattern Recognition: 42nd DAGM German Conference, DAGM GCPR 2020, T ¨ubingen, Germany, September 28–O...

    2020

  13. [21]

    Boreas: A multi- season autonomous driving dataset,

    K. Burnett, D. J. Yoon, Y . Wu, A. Z. Li, H. Zhang, S. Lu, J. Qian, W.-K. Tseng, A. Lambert, K. Y . Leung et al. , “Boreas: A multi- season autonomous driving dataset,” The International Journal of Robotics Research, vol. 42, no. 1-2, pp. 33–42, 2023

    2023

  14. [22]

    Subt-mrs dataset: Pushing slam towards all-weather environments,

    S. Zhao, Y . Gao, T. Wu, D. Singh, R. Jiang, H. Sun, M. Sarawata, Y . Qiu, W. Whittaker, I. Higgins et al., “Subt-mrs dataset: Pushing slam towards all-weather environments,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 22 647–22 657

    2024

  15. [23]

    M3ed: Multi-robot, multi-sensor, multi-environment event dataset,

    K. Chaney, F. Cladera, Z. Wang, A. Bisulco, M. A. Hsieh, C. Korpela, V . Kumar, C. J. Taylor, and K. Daniilidis, “M3ed: Multi-robot, multi-sensor, multi-environment event dataset,” in 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2023, ...

    2023

  16. [24]

    Heterogeneous lidar dataset for benchmarking robust localization in diverse degenerate scenarios,

    Z. Chen, Y . Qi, D. Feng, X. Zhuang, H. Chen, X. Hu, J. Wu, K. Peng, and P. Lu, “Heterogeneous lidar dataset for benchmarking robust localization in diverse degenerate scenarios,” arXiv preprint arXiv:2409.04961, 2024

    2024 arXiv

  17. [25]

    Fusionportable: A multi-sensor campus-scene dataset for evaluation of localization and mapping accuracy on diverse platforms,

    J. Jiao, H. Wei, T. Hu, X. Hu, Y . Zhu, Z. He, J. Wu, J. Yu, X. Xie, H. Huang et al. , “Fusionportable: A multi-sensor campus-scene dataset for evaluation of localization and mapping accuracy on diverse platforms,” in 2022 IEEE/RSJ International Conference on Intelligent Robot...

    2022

  18. [26]

    Fusionportablev2: A unified multi-sensor dataset for generalized slam across diverse platforms and scalable environments,

    H. Wei, J. Jiao, X. Hu, J. Yu, X. Xie, J. Wu, Y . Zhu, Y . Liu, L. Wang, and M. Liu, “Fusionportablev2: A unified multi-sensor dataset for generalized slam across diverse platforms and scalable environments,” arXiv preprint arXiv:2404.08563 , 2024

    2024 arXiv

  19. [27]

    Envodat: A large-scale multisen- sory dataset for robotic spatial awareness and semantic reasoning in heterogeneous environments,

    L. Nwankwo, B. Ellensohn, V . Dave, P. Hofer, J. Forstner, M. Vill- neuve, R. Galler, and E. Rueckert, “Envodat: A large-scale multisen- sory dataset for robotic spatial awareness and semantic reasoning in heterogeneous environments,” arXiv preprint arXiv:2410.22200 , 2024

  20. [28]

    Panoramis: An ultra- wide field of view image dataset for vision-based robot-motion estimation,

    H.-E. Benseddik, F. Morbidi, and G. Caron, “Panoramis: An ultra- wide field of view image dataset for vision-based robot-motion estimation,” The International Journal of Robotics Research , vol. 39, no. 9, pp. 1037–1051, 2020

    2020

  21. [29]

    Shang- haitech mapping robot is all you need: Robot system for collecting universal ground vehicle datasets,

    B. Xu, X. Zhao, D. Feng, Y . Yang, and S. Schwertfeger, “Shang- haitech mapping robot is all you need: Robot system for collecting universal ground vehicle datasets,” arXiv preprint arXiv:2406.16713, 2024

    2024 arXiv

  22. [30]

    Tartanair: A dataset to push the limits of visual slam,

    W. Wang, D. Zhu, X. Wang, Y . Hu, Y . Qiu, C. Wang, Y . Hu, A. Kapoor, and S. Scherer, “Tartanair: A dataset to push the limits of visual slam,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2020, pp. 4909– 4916

    2020

  23. [31]

    Wild-places: A large-scale dataset for lidar place recognition in unstructured natural environments,

    J. Knights, K. Vidanapathirana, M. Ramezani, S. Sridharan, C. Fookes, and P. Moghadam, “Wild-places: A large-scale dataset for lidar place recognition in unstructured natural environments,” in 2023 IEEE international conference on robotics and automation (ICRA). IEEE, 2023, pp...

    2023

  24. [32]

    Wildscenes: A benchmark for 2d and 3d semantic segmentation in large-scale natural environments,

    K. Vidanapathirana, J. Knights, S. Hausler, M. Cox, M. Ramezani, J. Jooste, E. Griffiths, S. Mohamed, S. Sridharan, C. Fookes, and P. Moghadam, “Wildscenes: A benchmark for 2d and 3d semantic segmentation in large-scale natural environments,” arXiv preprint arXiv:2312.15364, 2023

    2023 arXiv

  25. [33]

    The goose dataset for perception in unstructured environments,

    P. Mortimer, R. Hagmanns, M. Granero, T. Luettel, J. Petereit, and H.-J. Wuensche, “The goose dataset for perception in unstructured environments,” 2024

    2024

  26. [34]

    Finnforest dataset: A forest landscape for visual slam,

    I. Ali, A. Durmush, O. Suominen, J. Yli-Hietanen, S. Peltonen, J. Collin, and A. Gotchev, “Finnforest dataset: A forest landscape for visual slam,” Robotics and Autonomous Systems , vol. 132, p. 103610, 2020. IEEE TRANSACTIONS ON ROBOTICS - SPECIAL COLLECTION: VISUAL SLAM. PRE...

    2020

  27. [35]

    The sfu mountain dataset: Semi-structured woodland trails under changing environmental conditions,

    J. Bruce, J. Wawerla, and R. Vaughan, “The sfu mountain dataset: Semi-structured woodland trails under changing environmental conditions,” in IEEE International Conference on Robotics and Automation, 2015

    2015

  28. [36]

    OORD: The Oxford Offroad Radar Dataset,

    M. Gadd, D. De Martini, O. Bartlett, P. Murcutt, M. Towlson, M. Widojo, V . Mu s ¸at, L. Robinson, E. Panagiotaki, G. Pra- matarov, M. A. K”uhn, L. Marchegiani, P. Newman, and L. Kunze, “OORD: The Oxford Offroad Radar Dataset,” arXiv preprint arXiv:2403.02845, 2024

    2024 arXiv

  29. [37]

    Tartandrive: A large-scale dataset for learning off-road dynamics models,

    S. Triest, M. Sivaprakasam, S. J. Wang, W. Wang, A. M. Johnson, and S. Scherer, “Tartandrive: A large-scale dataset for learning off-road dynamics models,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 2546–2552

    2022

  30. [38]

    Tartandrive 2.0: More modalities and better infrastructure to further self-supervised learning research in off-road driving tasks,

    M. Sivaprakasam, P. Maheshwari, M. G. Castro, S. Triest, M. Nye, S. Willits, A. Saba, W. Wang, and S. Scherer, “Tartandrive 2.0: More modalities and better infrastructure to further self-supervised learning research in off-road driving tasks,” arXiv preprint arXiv:2402.01913, 2024

    2024 arXiv

  31. [39]

    Lidar-as- camera for end-to-end driving,

    A. Tampuu, R. Aidla, J. A. van Gent, and T. Matiisen, “Lidar-as- camera for end-to-end driving,” Sensors, vol. 23, no. 5, p. 2845, 2023

    2023

  32. [40]

    Grounded: A localizing ground penetrating radar evaluation dataset for learning to localize in inclement weather,

    T. Ort, I. Gilitschenski, and D. Rus, “Grounded: A localizing ground penetrating radar evaluation dataset for learning to localize in inclement weather,” The International Journal of Robotics Research , vol. 42, no. 10, pp. 901–916, 2023

    2023

  33. [41]

    Rellis-3d dataset: Data, benchmarks and analysis,

    P. Jiang, P. Osteen, M. Wigness, and S. Saripalli, “Rellis-3d dataset: Data, benchmarks and analysis,” in 2021 IEEE international conference on robotics and automation (ICRA) . IEEE, 2021, pp. 1110–1116

    2021

  34. [42]

    The rosario dataset: Multisensor data for localization and mapping in agricultural environments,

    T. Pire, M. Mujica, J. Civera, and E. Kofman, “The rosario dataset: Multisensor data for localization and mapping in agricultural environments,” The International Journal of Robotics Research , vol. 38, no. 6, pp. 633–641, 2019

    2019

  35. [43]

    Fieldsafe: dataset for obstacle detection in agriculture,

    M. F. Kragh, P. Christiansen, M. S. Laursen, M. Larsen, K. A. Steen, O. Green, H. Karstoft, and R. N. Jørgensen, “Fieldsafe: dataset for obstacle detection in agriculture,” Sensors, vol. 17, no. 11, p. 2579, 2017

    2017

  36. [44]

    Multimodal dataset for localization, mapping and crop monitoring in citrus tree farms,

    H. Teng, Y . Wang, X. Song, and K. Karydis, “Multimodal dataset for localization, mapping and crop monitoring in citrus tree farms,” in International Symposium on Visual Computing . Springer, 2023, pp. 571–582

    2023

  37. [45]

    Diter: Diverse terrain and multimodal dataset for field robot navigation in outdoor environments,

    S. Jeong, H. Kim, and Y . Cho, “Diter: Diverse terrain and multimodal dataset for field robot navigation in outdoor environments,” IEEE Sensors Letters, vol. 8, no. 3, pp. 1–4, 2024

    2024

  38. [46]

    Diter++: Diverse terrain and multi-modal dataset for multi-robot navigation in multi-session outdoor environments,

    J. Kim, S. Jeong, H. Kim, and Y . Cho, “Diter++: Diverse terrain and multi-modal dataset for multi-robot navigation in multi-session outdoor environments,” 2024

    2024

  39. [47]

    The newer college dataset: Handheld lidar, inertial and vision with ground truth,

    M. Ramezani, Y . Wang, M. Camurri, D. Wisth, M. Mattamala, and M. Fallon, “The newer college dataset: Handheld lidar, inertial and vision with ground truth,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2020, pp. 4353–4360

    2020

  40. [48]

    Multi-camera lidar inertial extension to the newer college dataset,

    L. Zhang, M. Camurri, D. Wisth, and M. Fallon, “Multi-camera lidar inertial extension to the newer college dataset,” arXiv preprint arXiv:2112.08854, 2021

    2021 arXiv

  41. [49]

    Botanicgarden: A high-quality dataset for robot navigation in unstructured natural environments,

    Y . Liu, Y . Fu, M. Qin, Y . Xu, B. Xu, F. Chen, B. Goossens, P. Z. Sun, H. Yu, C. Liu, L. Chen, W. Tao, and H. Zhao, “Botanicgarden: A high-quality dataset for robot navigation in unstructured natural environments,” IEEE Robotics and Automation Letters , vol. 9, no. 3, pp. 27...

    2024

  42. [50]

    Eden: Multimodal synthetic dataset of enclosed garden scenes,

    H.-A. Le, T. Mensink, P. Das, S. Karaoglu, and T. Gevers, “Eden: Multimodal synthetic dataset of enclosed garden scenes,” in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV), 2021, pp. 1578–1588

    2021

  43. [51]

    Tb-places: A data set for visual place recognition in garden environments

    M. Leyva-Vallina, N. Strisciuglio, M. L ´opez-Antequera, R. Tylecek, M. Blaich, and N. Petkov, “Tb-places: A data set for visual place recognition in garden environments.” IEEE Access, vol. 7, no. 52277- 52287, p. 2, 2019

    2019

  44. [52]

    Visual teach and repeat for long- range rover autonomy,

    P. Furgale and T. D. Barfoot, “Visual teach and repeat for long- range rover autonomy,” Journal of field robotics , vol. 27, no. 5, pp. 534–560, 2010

    2010

  45. [53]

    Method for registration of 3-d shapes,

    P. J. Besl and N. D. McKay, “Method for registration of 3-d shapes,” in Sensor fusion IV: control paradigms and data structures , vol

  46. [54]

    Unified temporal and spatial calibration for multi-sensor systems,

    P. Furgale, J. Rehder, and R. Siegwart, “Unified temporal and spatial calibration for multi-sensor systems,” in2013 IEEE/RSJ International Conference on Intelligent Robots and Systems , 2013, pp. 1280–1286

    2013

  47. [55]

    A benchmark for the evaluation of rgb-d slam systems,

    J. Sturm, N. Engelhard, F. Endres, W. Burgard, and D. Cremers, “A benchmark for the evaluation of rgb-d slam systems,” in 2012 IEEE/RSJ international conference on intelligent robots and systems . IEEE, 2012, pp. 573–580

    2012

  48. [56]

    Openvins: A research platform for visual-inertial estimation,

    P. Geneva, K. Eckenhoff, W. Lee, Y . Yang, and G. Huang, “Openvins: A research platform for visual-inertial estimation,” in 2020 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2020, pp. 4666–4672

    2020

  49. [57]

    A general optimization-based framework for local odometry estimation with multiple sensors,

    T. Qin, J. Pan, S. Cao, and S. Shen, “A general optimization-based framework for local odometry estimation with multiple sensors,” arXiv preprint arXiv:1901.03638 , 2019

    1901 arXiv

  50. [58]

    Orb-slam3: An accurate open-source library for visual, visual–inertial, and multimap slam,

    C. Campos, R. Elvira, J. J. G. Rodr ´ıguez, J. M. Montiel, and J. D. Tard´os, “Orb-slam3: An accurate open-source library for visual, visual–inertial, and multimap slam,” IEEE Transactions on Robotics , vol. 37, no. 6, pp. 1874–1890, 2021

    2021

  51. [59]

    Svo: Semidirect visual odometry for monocular and multicamera systems,

    C. Forster, Z. Zhang, M. Gassner, M. Werlberger, and D. Scaramuzza, “Svo: Semidirect visual odometry for monocular and multicamera systems,” IEEE Transactions on Robotics, vol. 33, no. 2, pp. 249–265, 2017

    2017

  52. [60]

    DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras,

    Z. Teed and J. Deng, “DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras,” Advances in neural information processing systems , 2021

    2021

  53. [61]

    Deep patch visual odometry,

    Z. Teed, L. Lipson, and J. Deng, “Deep patch visual odometry,” Advances in Neural Information Processing Systems , 2023

    2023

  54. [62]

    Deep Patch Visual SLAM,

    L. Lipson, Z. Teed, and J. Deng, “Deep Patch Visual SLAM,” in European Conference on Computer Vision , 2024

    2024

  55. [63]

    Bundle adjustment—a modern synthesis,

    B. Triggs, P. F. McLauchlan, R. I. Hartley, and A. W. Fitzgibbon, “Bundle adjustment—a modern synthesis,” in Vision Algorithms: Theory and Practice: International Workshop on Vision Algorithms Corfu, Greece, September 21–22, 1999 Proceedings . Springer, 2000, pp. 298–372

    1999

  56. [64]

    A tutorial on graph-based slam,

    G. Grisetti, R. K ¨ummerle, C. Stachniss, and W. Burgard, “A tutorial on graph-based slam,” IEEE Intelligent Transportation Systems Magazine, vol. 2, no. 4, pp. 31–43, 2010

    2010

  57. [65]

    A multi-state constraint kalman filter for vision-aided inertial navigation,

    A. I. Mourikis and S. I. Roumeliotis, “A multi-state constraint kalman filter for vision-aided inertial navigation,” in Proceedings 2007 IEEE International Conference on Robotics and Automation , 2007, pp. 3565–3572

    2007

  58. [66]

    Bags of binary words for fast place recognition in image sequences,

    D. G ´alvez-L´opez and J. D. Tard ´os, “Bags of binary words for fast place recognition in image sequences,” IEEE Transactions on Robotics, vol. 28, no. 5, pp. 1188–1197, 2012

    2012

  59. [67]

    Continual slam: Beyond lifelong simultaneous localization and mapping through continual learning,

    N. V ¨odisch, D. Cattaneo, W. Burgard, and A. Valada, “Continual slam: Beyond lifelong simultaneous localization and mapping through continual learning,” in The International Symposium of Robotics Research. Springer, 2022, pp. 19–35

    2022

  60. [68]

    Covio: Online continual learning for visual-inertial odometry,

    ——, “Covio: Online continual learning for visual-inertial odometry,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 2464–2473

    2023

  61. [69]

    Least-squares estimation of transformation parameters between two point patterns,

    S. Umeyama, “Least-squares estimation of transformation parameters between two point patterns,” IEEE Transactions on Pattern Analysis & Machine Intelligence , vol. 13, no. 04, pp. 376–380, 1991

    1991

  62. [70]

    A tutorial on quantitative trajectory evaluation for visual(-inertial) odometry,

    Z. Zhang and D. Scaramuzza, “A tutorial on quantitative trajectory evaluation for visual(-inertial) odometry,” in 2018 IEEE/RSJ Interna- tional Conference on Intelligent Robots and Systems (IROS) , 2018, pp. 7244–7251

    2018

  63. [71]

    evo: Python package for the evaluation of odometry and slam

    M. Grupp, “evo: Python package for the evaluation of odometry and slam.” https://github.com/MichaelGrupp/evo, 2017. IEEE TRANSACTIONS ON ROBOTICS - SPECIAL COLLECTION: VISUAL SLAM. PREPRINT VERSION. 19 Fabian Schmidt received his M.S. degree in Ap- plied Computer Science from ...

    2017

  64. [1611]

    Spie, 1992, pp. 586–606

    1992

  65. [2005]

    He received the Ph.D

    In 2002 and 2003 he was a visiting student researcher at the Centre for Vision Research at York University, Toronto, Canada. He received the Ph.D. degree in Computer Science from the University of Heidelberg in 2011. From 2010 to 2020, he has been working with Mercedes-Benz Re...

    2002

  66. [2017]

    From 2018 he is working in the robotics research and development department of STIHL

    His research focused on path planning and environmental sensing for mobile robots. From 2018 he is working in the robotics research and development department of STIHL. Constantin Blessing received his M.S. degree in Applied Computer Science from Esslingen University of Applie...

    2018

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.