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Wildcat: Online Continuous-Time 3D Lidar-Inertial SLAM

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arxiv 2205.12595 v1 pith:ZT3UDN5G submitted 2022-05-25 cs.RO

Milad Ramezani , Kasra Khosoussi , Gavin Catt , Peyman Moghadam , Jason Williams , Paulo Borges , Fred Pauling , Navinda Kottege This is my paper · ORCID
classification cs.RO
keywords wildcatlidar-inertialslamrobustnesscontinuous-timemoduleonlinesystems
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We present Wildcat, a novel online 3D lidar-inertial SLAM system with exceptional versatility and robustness. At its core, Wildcat combines a robust real-time lidar-inertial odometry module, utilising a continuous-time trajectory representation, with an efficient pose-graph optimisation module that seamlessly supports both the single- and multi-agent settings. The robustness of Wildcat was recently demonstrated in the DARPA Subterranean Challenge where it outperformed other SLAM systems across various types of sensing-degraded and perceptually challenging environments. In this paper, we extensively evaluate Wildcat in a diverse set of new and publicly available real-world datasets and showcase its superior robustness and versatility over two existing state-of-the-art lidar-inertial SLAM systems.

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Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. GemNav: Discrete-Token Visual Robot Navigation using a Multimodal Large Language Model

    cs.RO 2026-07 conditional novelty 6.0 of 10

    A frozen MLLM with LoRA on language layers alone, trained on 8.7 hours of data, navigates four unseen real-world environments using discrete waypoint tokens and stops within 0.42m of goals.

  2. GaRLILEO: Gravity-aligned Radar-Leg-Inertial Enhanced Odometry

    cs.RO 2025-11 conditional novelty 6.0 of 10

    A continuous-time radar-leg-inertial odometry with a soft S2 gravity factor reduces vertical drift in legged robots, achieving sub-meter z-error on 12 real-world sequences.

  3. AEOS: Active Environment-aware Optimal Scanning Control for UAV LiDAR-Inertial Odometry in Complex Scenes

    cs.RO 2025-09 conditional novelty 6.0 of 10

    AEOS actively rotates a UAV's LiDAR using a hybrid MPC and learned cost map, cutting trajectory error versus fixed-speed and optimization-only baselines in simulations and two real scenes.

  4. A Comprehensive Evaluation of LiDAR Odometry Techniques

    cs.RO 2025-07 conditional novelty 6.0 of 10

    A seven-dataset ablation shows planar features with plane-to-plane residuals, IMU dewarping, and constant velocity initialization work best for LiDAR odometry.

  5. GaRLIO: Gravity enhanced Radar-LiDAR-Inertial Odometry

    cs.RO 2025-02 conditional novelty 6.0 of 10

    GaRLIO uses radar Doppler velocity measurements to estimate gravity and reduce vertical drift in LiDAR-inertial odometry, outperforming three LIO baselines on public datasets.

  6. Online Adaptive Traversability Estimation through Interaction for Unstructured, Densely Vegetated Environments

    cs.RO 2025-02 conditional novelty 6.0 of 10

    A robot can train a 3D voxel-based traversability model from scratch in situ from self-supervised collision data in about eight minutes, achieving MCC 0.63 and enabling safe point-to-point navigation in dense vegetation.

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