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

TL;DR

This paper introduces 3DRO, a radar odometry method that combines 2D imaging radar data with gyroscope measurements to achieve lidar-level 3D ego-motion estimation.

Contribution

It extends the existing SE(2) radar odometry framework to SE(3) by integrating 3D gyroscope data, enabling full 3D pose estimation from 2D radar data.

Findings

Achieves lidar-level odometry accuracy on 643km of data

Successfully estimates 3D ego-motion using 2D radar and gyroscope data

Extends SE(2) radar odometry to full SE(3) estimation

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.