RaDiVe: Robust 4D Radar Odometry with Distance-Bounded NDT and Velocity-Discrepancy Point Uncertainty

• URL: http://arxiv.org/abs/2607.28045v1
• Date: Thu, 30 Jul 2026 11:26:41 GMT
• Title: RaDiVe: Robust 4D Radar Odometry with Distance-Bounded NDT and Velocity-Discrepancy Point Uncertainty
• Abstract summary: RaDiVe is a 4D radar odometry framework designed to improve the accuracy and robustness of radar point-cloud registration.<n>We introduce a distance-bounded Normal Distributions Transform (NDT), which improves optimization stability and computational efficiency.<n>We incorporate Signed Distance Function (SDF)-based surface point extraction via implicit neural mapping to construct a geometrically consistent and noise-filtered local submap.
• Score: 12.2982667950681
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recent advances in 4D radar enable robust perception in adverse weather; however, the inherent sparsity, noise, and limited positional precision of radar point clouds pose significant challenges for registration-based odometry. In this letter, we propose RaDiVe, a 4D radar odometry framework designed to improve the accuracy and robustness of radar point-cloud registration. We introduce a distance-bounded Normal Distributions Transform (NDT), which improves optimization stability and computational efficiency by restricting the correspondence search to near-distance voxel pairs. To mitigate measurement ambiguity, we propose a velocity-discrepancy point uncertainty model that weights each input 4D radar point according to the discrepancy between its measured Doppler radial velocity and the radial velocity predicted from the estimated ego-velocity. Furthermore, we incorporate Signed Distance Function (SDF)-based surface point extraction via implicit neural mapping to construct a geometrically consistent and noise-filtered local submap. Evaluations across multiple public datasets demonstrate that RaDiVe outperforms existing 4D radar odometry baselines by 44.4% in translational Absolute Trajectory Error (ATE) and 21.3% in rotational ATE on average, while maintaining real-time performance. The source code will be made publicly available to the robotics community: https://github.com/to-be-open-sourced.

Related papers

• RbFT-Net: Rectify-Before-Fuse Temporal Radar Anchors for 4D Radar-Camera Depth Completion [20.340336916195053]
  Radar measurements are inherently sparse and susceptible to clutter, multipath reflections, and projection errors.<n>We propose RbFT-Net, an end-to-end framework for multi-frame 4D radar-camera depth completion.<n> Experiments on ZJU-4DRadarCam and a newly collected 4D radar-camera-LiDAR dataset show that RbFT-Net consistently outperforms independent radar-camera methods.
  arXiv  Detail & Related papers  (2026-08-13T11:26:46Z)
• RAMBA: 4D Radar Mapping by Bundle Adjustment [3.8603693015738205]
  This paper presents RAMBA, a radar bundle-adjustment framework for globally consistent 4D radar mapping.<n>Experiments on the ColoRadar and SNAIL Radar datasets show that RAMBA improves map consistency and usually enhances trajectory accuracy.
  arXiv  Detail & Related papers  (2026-05-24T12:38:18Z)
• 4D Radar Gaussian Modeling and Scan Matching with RCS [80.74019565026542]
  Radar Cross Section (RCS) information is often overlooked and not usually used in modeling and scan matching processes.<n>We propose incorporating the physical behavior of RCS in the model, in order to further enrich the summarized information about the scene.
  arXiv  Detail & Related papers  (2026-04-16T10:58:29Z)
• UNRIO: Uncertainty-Aware Velocity Learning for Radar-Inertial Odometry [16.577608323279662]
  UNRIO is an uncertainty-aware radar-inertial odometry system.<n>It estimates ego-velocity directly from raw mmWave radar IQ signals.
  arXiv  Detail & Related papers  (2026-04-15T07:43:06Z)
• Equi-RO: A 4D mmWave Radar Odometry via Equivariant Networks [19.594498796044835]
  We introduce Equi-RO, an equivariant network-based framework for 4D radar odometry.<n>A graph-based architecture enhances feature aggregation in sparse radar data.<n> Experiments on the open-source dataset and self-collected dataset show Equi-RO outperforms state-of-the-art algorithms in accuracy and robustness.
  arXiv  Detail & Related papers  (2025-09-25T02:21:05Z)
• TacoDepth: Towards Efficient Radar-Camera Depth Estimation with One-stage Fusion [54.46664104437454]
  We propose TacoDepth, an efficient and accurate Radar-Camera depth estimation model with one-stage fusion.<n>Specifically, the graph-based Radar structure extractor and the pyramid-based Radar fusion module are designed.<n>Compared with the previous state-of-the-art approach, TacoDepth improves depth accuracy and processing speed by 12.8% and 91.8%.
  arXiv  Detail & Related papers  (2025-04-16T05:25:04Z)
• RobuRCDet: Enhancing Robustness of Radar-Camera Fusion in Bird's Eye View for 3D Object Detection [68.99784784185019]
  Poor lighting or adverse weather conditions degrade camera performance.<n>Radar suffers from noise and positional ambiguity.<n>We propose RobuRCDet, a robust object detection model in BEV.
  arXiv  Detail & Related papers  (2025-02-18T17:17:38Z)
• Echoes Beyond Points: Unleashing the Power of Raw Radar Data in Multi-modality Fusion [74.84019379368807]
  We propose a novel method named EchoFusion to skip the existing radar signal processing pipeline. Specifically, we first generate the Bird's Eye View (BEV) queries and then take corresponding spectrum features from radar to fuse with other sensors.
  arXiv  Detail & Related papers  (2023-07-31T09:53:50Z)
• SMURF: Spatial Multi-Representation Fusion for 3D Object Detection with 4D Imaging Radar [12.842457981088378]
  This paper introduces spatial multi-representation fusion (SMURF), a novel approach to 3D object detection using a single 4D imaging radar. SMURF mitigates measurement inaccuracy caused by limited angular resolution and multi-path propagation of radar signals. Experimental evaluations on View-of-Delft (VoD) and TJ4DRadSet datasets demonstrate the effectiveness and generalization ability of SMURF.
  arXiv  Detail & Related papers  (2023-07-20T11:33:46Z)
• Semantic Segmentation of Radar Detections using Convolutions on Point Clouds [59.45414406974091]
  We introduce a deep-learning based method to convolve radar detections into point clouds. We adapt this algorithm to radar-specific properties through distance-dependent clustering and pre-processing of input point clouds. Our network outperforms state-of-the-art approaches that are based on PointNet++ on the task of semantic segmentation of radar point clouds.
  arXiv  Detail & Related papers  (2023-05-22T07:09:35Z)
• LiRaNet: End-to-End Trajectory Prediction using Spatio-Temporal Radar Fusion [52.59664614744447]
  We present LiRaNet, a novel end-to-end trajectory prediction method which utilizes radar sensor information along with widely used lidar and high definition (HD) maps. automotive radar provides rich, complementary information, allowing for longer range vehicle detection as well as instantaneous velocity measurements.
  arXiv  Detail & Related papers  (2020-10-02T00:13:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.