Co-RaL: Complementary Radar-Leg Odometry with 4-DoF Optimization and Rolling Contact

TL;DR

This paper presents Co-RaL, a novel sensor fusion approach combining radar and leg odometry with 4-DoF optimization and rolling contact modeling to improve localization accuracy and robustness in challenging environments.

Contribution

It introduces a tightly coupled radar-leg odometry algorithm with 4-DoF optimization and decoupled RANSAC, enhancing radar odometry and accommodating contact drift and radar failure.

Findings

Enhanced radar odometry accuracy, especially in z-direction.

Robust localization in diverse environments with sensor failure scenarios.

Open dataset for benchmarking radar-leg odometry methods.

Abstract

Robust and accurate localization in challenging environments is becoming crucial for SLAM. In this paper, we propose a unique sensor configuration for precise and robust odometry by integrating chip radar and a legged robot. Specifically, we introduce a tightly coupled radar-leg odometry algorithm for complementary drift correction. Adopting the 4-DoF optimization and decoupled RANSAC to mmWave chip radar significantly enhances radar odometry beyond the existing method, especially z-directional even when using a single radar. For the leg odometry, we employ rolling contact modeling-aided forward kinematics, accommodating scenarios with the potential possibility of contact drift and radar failure. We evaluate our method by comparing it with other chip radar odometry algorithms using real-world datasets with diverse environments while the datasets will be released for the robotics community. https://github.com/SangwooJung98/Co-RaL-Dataset