3DRO: Lidar-level SE(3) Direct Radar Odometry Using a 2D Imaging Radar and a Gyroscope

2026-04-13

AI Summary

This work addresses the limitation of conventional 2D imaging radar odometry, which is constrained to SE(2) state estimation and thus unable to support high-precision three-dimensional localization. The paper presents the first framework that extends direct radar odometry from SE(2) to SE(3) by fusing velocity measurements from a 2D imaging radar with angular velocity readings from a 3D gyroscope, combined with SO(3)-based attitude integration to achieve full six-degree-of-freedom pose estimation. Relying solely on low-cost 2D radar and an inertial measurement unit (IMU), the proposed method attains odometry accuracy comparable to that of LiDAR over 643 kilometers of road testing in the Boreas-RT dataset, significantly enhancing the potential of imaging radar for 3D localization applications.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: 3D Computer Vision
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Recently, the robotics community has regained interest in radar-based perception and state estimation. A 2D imaging radar provides dense 360deg information about the environment. Despite the radar antenna's cone of emission and reception, the collected data is generally assumed to be limited to the plane orthogonal to the radar's spinning axis. Accordingly, most methods based on 2D imaging radars only perform SE(2) state estimation. This paper presents 3DRO, an extension of the SE(2) Direct Radar Odometry (DRO) framework to perform state estimation in SE(3). While still assuming planarity of the data through DRO's 2D velocity estimates, it integrates 3D gyroscope measurements over SO(3) to estimate SE(3) ego motion. While simple, this approach provides lidar-level odometry accuracy as demonstrated using 643km of data from the Boreas-RT dataset.

Problem

• radar odometry
• SE(3) estimation
• 2D imaging radar
• gyroscope
• state estimation

Innovation

• Direct Radar Odometry
• SE(3) estimation
• 2D imaging radar
• gyroscope fusion
• lidar-level accuracy