Pushing Radar Odometry Beyond the Pavement: Current Capabilities and Challenges
Shaunak Kolhe, Peng Jiang, Maggie Wigness, Philip Osteen, Timothy Overbye + 2 more
TLDR
This paper investigates off-road radar odometry, detailing challenges and proposing Radar-KISSICP and Radar-IMU baselines for improved estimation.
Key contributions
- Investigates radar odometry challenges in off-road environments, including full SE(3) motion and sparse features.
- Introduces Radar-KISSICP, using motion compensation for 3D-aware radar pointclouds.
- Proposes Radar-IMU, which leverages IMU preintegration to stabilize scan matching.
- Demonstrates improved trajectory estimation on the GO dataset, setting a new off-road radar odometry baseline.
Why it matters
Radar is crucial for localization in challenging, unstructured environments due to its robustness to weather and lighting. This paper addresses the gap in understanding radar odometry performance off-road, providing foundational baselines and identifying key challenges for future research in off-road robotics.
Original Abstract
Radar offers unique advantages for localization in unstructured environments, including robustness to weather, lighting, and airborne particulates. While most prior work has studied radar odometry in urban, largely planar settings, its performance in off-road environments remains less understood. In this paper, we investigate the potential of radar for off-road odometry estimation and identify key challenges that arise from full $SE(3)$ vehicle motion, terrain-induced ground returns, and sparse or unstable features. To address these issues, we introduce two simple baselines: Radar-KISSICP, which applies motion compensation to generate 3D-aware radar pointclouds, and Radar-IMU, which leverages IMU preintegration to stabilize scan matching. Experiments on the Great Outdoors (GO) dataset demonstrate that these baselines improve trajectory estimation in challenging routes and provide a reference point for future development of radar odometry in off-road robotics.
📬 Weekly AI Paper Digest
Get the top 10 AI/ML arXiv papers from the week — summarized, scored, and delivered to your inbox every Monday.