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Paper Citation Record · LEDGER

Ground-Optimized 4D Radar-Inertial Odometry via Continuous Velocity Integration using Gaussian Process

As of 18 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2502.08093.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2502.08093 v2

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T10:55:20.514264Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

32 of 32 outbound references displayed

  • verified exact0
  • verified fuzzy31
  • unresolved1
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation efc60f1d-c16c-4cb7-8c16-343838086003 · outbound

This paper cites A New Wave in Robotics: Survey on Recent mmWave Radar Applications in Robotics,.

Ground-Optimized 4D Radar-Inertial Odometry via Continuous Velocity Integration using Gaussian Process A New Wave in Robotics: Survey on Recent mmWave Radar Applications in Robotics,

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T10:55:20.878210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-08T10:55:20.398127Z digest=sha256:61acb39ffc269b3076f83dcdb737b9b592cbf30bdcac6dd19e46652ffa4b95ad

Observation 0639f348-2d65-41b0-87a5-be8ecf26f32a · outbound

This paper cites Radar Odometry for Autonomous Ground Vehicles: A Survey of Methods and Datasets,.

Ground-Optimized 4D Radar-Inertial Odometry via Continuous Velocity Integration using Gaussian Process Radar Odometry for Autonomous Ground Vehicles: A Survey of Methods and Datasets,

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T10:55:20.867464Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-08T10:55:20.402789Z digest=sha256:795b30fb3d95b714eb8d836c3e1aaf53842158846a47ffc15dacea594c62c0c2

Observation 18e73ff3-9e69-4870-9c3a-d798b4263368 · outbound

This paper cites Evaluation of Navigation Sen- sors in Fire Smoke Environments,.

Ground-Optimized 4D Radar-Inertial Odometry via Continuous Velocity Integration using Gaussian Process Evaluation of Navigation Sen- sors in Fire Smoke Environments,

Reference 3

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verified fuzzy
raw_fallback, observed 2026-08-08T10:55:20.856371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-08T10:55:20.406392Z digest=sha256:17970256b91bc6ebf96af9be87c924ea2700936e76c8d9f8b5b53c903b8eef55

Observation 2376ada9-777b-4ec3-b9ee-810f9cea62a1 · outbound

This paper cites A Benchmark for Lidar Sensors in Fog: Is Detection Breaking Down?.

Ground-Optimized 4D Radar-Inertial Odometry via Continuous Velocity Integration using Gaussian Process A Benchmark for Lidar Sensors in Fog: Is Detection Breaking Down?

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T10:55:20.845509Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-08T10:55:20.410168Z digest=sha256:befc2668228aeb44362054b93fec599252493495d9e710a76f1aa93abab493f0

Observation 042c6b6b-b447-48a2-a22b-028974499056 · outbound

This paper cites Radar-inertial state estimation and obstacle detection for micro-aerial vehicles in dense fog,.

Ground-Optimized 4D Radar-Inertial Odometry via Continuous Velocity Integration using Gaussian Process Radar-inertial state estimation and obstacle detection for micro-aerial vehicles in dense fog,

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T10:55:20.834874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-08T10:55:20.413879Z digest=sha256:7f04ef2e3438878b7237821ddf676338071c0027d02388b71df128bb5076ee31

Observation a4cbaf23-2488-49cb-b193-dc0cfac541be · outbound

This paper cites Degradation Resilient LiDAR-Radar-Inertial Odometry.

Ground-Optimized 4D Radar-Inertial Odometry via Continuous Velocity Integration using Gaussian Process Degradation Resilient LiDAR-Radar-Inertial Odometry

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-08T10:55:20.417882Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T10:55:20.417882Z digest=sha256:52bc9fadd6c26c2920c96d6eea91c4feb4300de5b3a4760cddde30662d0f99f5

Observation 191c0e8c-5cdf-44fa-8085-f47e6b51b064 · outbound

This paper cites Lidar-Level Localization With Radar? The CFEAR Approach to Accurate, Fast, and Robust Large- Scale Radar Odometry in Diverse Environments,.

Ground-Optimized 4D Radar-Inertial Odometry via Continuous Velocity Integration using Gaussian Process Lidar-Level Localization With Radar? The CFEAR Approach to Accurate, Fast, and Robust Large- Scale Radar Odometry in Diverse Environments,

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T10:55:20.824361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-08T10:55:20.423237Z digest=sha256:2ceeeff820595212adaf53b0856fe343c7a0c52079a233183881f22318dac58e

Observation 579fb46c-b108-4c62-9501-df10ad8d7dac · outbound

This paper cites LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain,.

Ground-Optimized 4D Radar-Inertial Odometry via Continuous Velocity Integration using Gaussian Process LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain,

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T10:55:20.813690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-08T10:55:20.426884Z digest=sha256:a28c89a291a1772f536d9ee6b3d2507444340f27096ed0c100d7874e57a9f716

Observation 0378d093-2988-4cf4-8f2b-55dcd1c59269 · outbound

This paper cites MULLS: Versatile LiDAR SLAM via Multi-metric Linear Least Square,.

Ground-Optimized 4D Radar-Inertial Odometry via Continuous Velocity Integration using Gaussian Process MULLS: Versatile LiDAR SLAM via Multi-metric Linear Least Square,

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T10:55:20.803424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-08T10:55:20.430300Z digest=sha256:3fb241233bab87d56f9d7620c51671c7eda0aff0b6a78538bd6f4f4b3a881033

Observation f8dbf063-f195-45c6-9b68-fb9b8ff62f4b · outbound

This paper cites Efficient LiDAR odometry for Au- tonomous Driving,.

Ground-Optimized 4D Radar-Inertial Odometry via Continuous Velocity Integration using Gaussian Process Efficient LiDAR odometry for Au- tonomous Driving,

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T10:55:20.793395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-08T10:55:20.433831Z digest=sha256:ee17a9e066f8ce0ccf90b5fe637769647d01431cd8c8fddd242b85b4cc095900

Observation f3a06237-1ce5-4cde-9dd6-e9fbbcda8e85 · outbound

This paper cites Low-Drift Odometry, Mapping and Ground Segmentation Using a Backpack LiDAR System,.

Ground-Optimized 4D Radar-Inertial Odometry via Continuous Velocity Integration using Gaussian Process Low-Drift Odometry, Mapping and Ground Segmentation Using a Backpack LiDAR System,

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T10:55:20.783143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-08T10:55:20.437331Z digest=sha256:200904b86c21de7c04b15c318c9f819ed6918610818e66278ba885f2769b145d

Observation 8d7c9eea-7714-4f2f-aad8-66adf03b4889 · outbound

This paper cites GCLO: Ground Constrained LiDAR Odometry with Low-drifts for GPS- denied Indoor Environments,.

Ground-Optimized 4D Radar-Inertial Odometry via Continuous Velocity Integration using Gaussian Process GCLO: Ground Constrained LiDAR Odometry with Low-drifts for GPS- denied Indoor Environments,

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T10:55:20.773153Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-08T10:55:20.441350Z digest=sha256:b11e8b307bd413d0299712fbe15a7f7e1d62c2d8ee2127cc0a5cc66a666db2e0

Observation 24242a37-3254-4c7b-a029-f76ebe211a4f · outbound

This paper cites GND-LO: Ground Decoupled 3D Lidar Odometry Based on Planar Patches,.

Ground-Optimized 4D Radar-Inertial Odometry via Continuous Velocity Integration using Gaussian Process GND-LO: Ground Decoupled 3D Lidar Odometry Based on Planar Patches,

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T10:55:20.762955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-08T10:55:20.445067Z digest=sha256:278d2f04efa54ec9bb31c92efc1a2b1d5920c4d4d5695ea12f93b95dc507490e

Observation e717b56f-2b7a-4815-aa66-441eff0f03ce · outbound

This paper cites 4D Radar-Based Pose Graph SLAM With Ego-Velocity Pre-Integration Factor,.

Ground-Optimized 4D Radar-Inertial Odometry via Continuous Velocity Integration using Gaussian Process 4D Radar-Based Pose Graph SLAM With Ego-Velocity Pre-Integration Factor,

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T10:55:20.752831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-08T10:55:20.448583Z digest=sha256:48c3e8d6eed49ca2317954fa657faf7ecbac7cf2e74b48465c139ed292dc9d41

Observation 06288610-5477-4f37-8390-ad834f0b1bc8 · outbound

This paper cites DRIO: Robust Radar- Inertial Odometry in Dynamic Environments,.

Ground-Optimized 4D Radar-Inertial Odometry via Continuous Velocity Integration using Gaussian Process DRIO: Robust Radar- Inertial Odometry in Dynamic Environments,

Reference 15

Resolution
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raw_fallback, observed 2026-08-08T10:55:20.742913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-08T10:55:20.452086Z digest=sha256:57450b002295e25286c8b6c180ce35a5513bb3f4b98f72b9ad35a53057c493d1

Observation 525eb770-488b-4574-b57e-9384e0dc403c · outbound

This paper cites Instantaneous ego-motion estimation using Doppler radar,.

Ground-Optimized 4D Radar-Inertial Odometry via Continuous Velocity Integration using Gaussian Process Instantaneous ego-motion estimation using Doppler radar,

Reference 16

Resolution
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raw_fallback, observed 2026-08-08T10:55:20.732424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-08T10:55:20.455515Z digest=sha256:a90557af5c11be86dff15754bdbea95dbff2d76cb3fa7fac556cebfae7c82209

Observation 927ea528-b8e0-4d2b-95ca-5b0dc6a91cae · outbound

This paper cites Radar-Inertial Ego-Velocity Estimation for Visually Degraded Environments,.

Ground-Optimized 4D Radar-Inertial Odometry via Continuous Velocity Integration using Gaussian Process Radar-Inertial Ego-Velocity Estimation for Visually Degraded Environments,

Reference 17

Resolution
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raw_fallback, observed 2026-08-08T10:55:20.721781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-08T10:55:20.459239Z digest=sha256:0a2ef7d0928555dae01e44e40753502d3932cfe1f3f79922d9167f20def201f6

Observation 1734b968-5b57-449b-8695-d9a0a7c4a72e · outbound

This paper cites A Credible and Robust Approach to Ego-Motion Estimation Using an Automotive Radar,.

Ground-Optimized 4D Radar-Inertial Odometry via Continuous Velocity Integration using Gaussian Process A Credible and Robust Approach to Ego-Motion Estimation Using an Automotive Radar,

Reference 18

Resolution
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raw_fallback, observed 2026-08-08T10:55:20.711141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-08T10:55:20.462767Z digest=sha256:80e8351141f4209d9ac42f2e72c376bf8f58c0da0dc33f27af652d3680b474c6

Observation 7fa29427-53b6-46f5-8292-4b86ff2b1e27 · outbound

This paper cites Radar inertial odometry with on- line calibration,.

Ground-Optimized 4D Radar-Inertial Odometry via Continuous Velocity Integration using Gaussian Process Radar inertial odometry with on- line calibration,

Reference 19

Resolution
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raw_fallback, observed 2026-08-08T10:55:20.701151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-08T10:55:20.466587Z digest=sha256:1b970ab749c46ce101a44feceb5fe1949b37857745e07c70a9b31f3a0a7ef605

Observation e7d09b2c-7bf1-486f-ba92-0dc82a6d03b3 · outbound

This paper cites 3D ego- Motion Estimation Using low-Cost mmWave Radars via Radar Velocity Factor for Pose-Graph SLAM,.

Ground-Optimized 4D Radar-Inertial Odometry via Continuous Velocity Integration using Gaussian Process 3D ego- Motion Estimation Using low-Cost mmWave Radars via Radar Velocity Factor for Pose-Graph SLAM,

Reference 20

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raw_fallback, observed 2026-08-08T10:55:20.691141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-08T10:55:20.470217Z digest=sha256:1d54ff2a97db852d02525cca8ed6b4f070da68f7ce3ce1320de993f1a54f2875

Observation 8a1cbd59-b03c-48e4-a8c7-d42965dfb4a3 · outbound

This paper cites Tightly-Coupled EKF- Based Radar-Inertial Odometry,.

Ground-Optimized 4D Radar-Inertial Odometry via Continuous Velocity Integration using Gaussian Process Tightly-Coupled EKF- Based Radar-Inertial Odometry,

Reference 21

Resolution
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raw_fallback, observed 2026-08-08T10:55:20.680840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-08T10:55:20.473999Z digest=sha256:05518dd9ded54e9f7dc0c88f5a46f674db7b000d8e4b6a529da50a7960663aee

Observation ecdf00cd-6afd-4559-8d6b-532fefaa98cc · outbound

This paper cites 4D iRIOM: 4D Imaging Radar Inertial Odometry and Mapping,.

Ground-Optimized 4D Radar-Inertial Odometry via Continuous Velocity Integration using Gaussian Process 4D iRIOM: 4D Imaging Radar Inertial Odometry and Mapping,

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T10:55:20.670837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-08T10:55:20.477530Z digest=sha256:ccfff11288de70edebe23d2ea7b6d92e1b33c33c512a382b5d42205e11cefd87

Observation 28225604-ee1a-46e1-9a9c-6ac98c683932 · outbound

This paper cites DeRO: Dead Reckoning Based on Radar Odometry With Accelerometers Aided for Robot Localization,.

Ground-Optimized 4D Radar-Inertial Odometry via Continuous Velocity Integration using Gaussian Process DeRO: Dead Reckoning Based on Radar Odometry With Accelerometers Aided for Robot Localization,

Reference 23

Resolution
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raw_fallback, observed 2026-08-08T10:55:20.660205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-08T10:55:20.481047Z digest=sha256:7b8b3a623e480c2de8a19529801fd7aa2d0ad657cb36d4ba9dafb22b1c0e3906

Observation 8fcad008-1255-4762-a463-78a7a7498c1c · outbound

This paper cites Continuous Integration over SO (3) for IMU Preintegration,.

Ground-Optimized 4D Radar-Inertial Odometry via Continuous Velocity Integration using Gaussian Process Continuous Integration over SO (3) for IMU Preintegration,

Reference 24

Resolution
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raw_fallback, observed 2026-08-08T10:55:20.649047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-08T10:55:20.484776Z digest=sha256:66ba3779da731eb0d9d5836a57478c0360f13597659de08481f1e87b5578fb3c

Observation de8aaca8-0920-40a0-8258-34df334d1238 · outbound

This paper cites Continuous-time Radar-inertial Odometry for Automotive Radars,.

Ground-Optimized 4D Radar-Inertial Odometry via Continuous Velocity Integration using Gaussian Process Continuous-time Radar-inertial Odometry for Automotive Radars,

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T10:55:20.637782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-08T10:55:20.488303Z digest=sha256:35c67b16413a049113854f06be785eb900bcdaa353464849a3d3167197e203c6

Observation 1a61b802-7797-4b75-85a0-fe189b7380d1 · outbound

This paper cites Patchwork++: Fast and Robust Ground Segmentation Solving Partial Under- Segmentation Using 3D Point Cloud,.

Ground-Optimized 4D Radar-Inertial Odometry via Continuous Velocity Integration using Gaussian Process Patchwork++: Fast and Robust Ground Segmentation Solving Partial Under- Segmentation Using 3D Point Cloud,

Reference 26

Resolution
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raw_fallback, observed 2026-08-08T10:55:20.627033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-08T10:55:20.491731Z digest=sha256:91cfa98c22d6b6f0680ff9a1e72dedd8d5d4653feb3c57be9635d61b1b6689fa

Observation d794fedf-161b-4560-977f-574fc8405b53 · outbound

This paper cites G2o: A general framework for graph optimiza- tion,.

Ground-Optimized 4D Radar-Inertial Odometry via Continuous Velocity Integration using Gaussian Process G2o: A general framework for graph optimiza- tion,

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T10:55:20.614857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-08T10:55:20.495113Z digest=sha256:539641cff0334194e8603a0af510f63a53bf12704fd391545f9e46895315d701

Observation 8484c673-04f7-4c09-9faa-e3c37070caab · outbound

This paper cites NTU4DRadLM: 4D Radar-Centric Multi-Modal Dataset for Localization and Mapping,.

Ground-Optimized 4D Radar-Inertial Odometry via Continuous Velocity Integration using Gaussian Process NTU4DRadLM: 4D Radar-Centric Multi-Modal Dataset for Localization and Mapping,

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T10:55:20.603390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-08T10:55:20.498667Z digest=sha256:52d05355183eea63ad9684288c44bf49cf5565c5ffe58ee362e9a5947e47bbca

Observation 321279a7-422c-4915-a2c5-1fb11788393e · outbound

This paper cites MSC-RAD4R: ROS-Based Automotive Dataset With 4D Radar,.

Ground-Optimized 4D Radar-Inertial Odometry via Continuous Velocity Integration using Gaussian Process MSC-RAD4R: ROS-Based Automotive Dataset With 4D Radar,

Reference 29

Resolution
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raw_fallback, observed 2026-08-08T10:55:20.592570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-08T10:55:20.502527Z digest=sha256:8e8c01e9cc818ae0f1bd89e5579cfbe50a666e979f032dcf37e8e2dc4a5fcb72

Observation 99e7c81f-3303-432f-a718-0e926a162f15 · outbound

This paper cites 4DRadarSLAM: A 4D Imaging Radar SLAM System for Large-scale Environments based on Pose Graph Optimization,.

Ground-Optimized 4D Radar-Inertial Odometry via Continuous Velocity Integration using Gaussian Process 4DRadarSLAM: A 4D Imaging Radar SLAM System for Large-scale Environments based on Pose Graph Optimization,

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T10:55:20.581386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-08T10:55:20.506381Z digest=sha256:db14daf39a0a10ca8cf2db476c51db835f6b31ee1858caa2038cd986e597d32d

Observation d96d3156-d3d9-481a-9751-edf890bed658 · outbound

This paper cites Evo: Python package for the evaluation of odom- etry and slam,.

Ground-Optimized 4D Radar-Inertial Odometry via Continuous Velocity Integration using Gaussian Process Evo: Python package for the evaluation of odom- etry and slam,

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T10:55:20.570008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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This paper cites Do we need scan- matching in radar odometry?.

Ground-Optimized 4D Radar-Inertial Odometry via Continuous Velocity Integration using Gaussian Process Do we need scan- matching in radar odometry?

Reference 32

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source=pdf_text observed=2026-08-08T10:55:20.514264Z digest=sha256:c51e651d0e683c5b4628198d282ec9a20acd1d895d528985b9de74ab55d1a568

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