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

翻译：在挑战性环境中实现鲁棒且精确的定位正变得对SLAM至关重要。本文提出一种独特的传感器配置，通过集成芯片雷达与腿足机器人，实现精确且鲁棒的里程计。具体而言，我们引入一种紧耦合的雷达-腿足里程计算法，以实现互补的漂移校正。采用四自由度优化和解耦RANSAC处理毫米波芯片雷达，显著提升了雷达里程计性能，超越了现有方法，尤其是在使用单个雷达时对z方向估计的改进。对于腿足里程计，我们采用滚动接触建模辅助的前向运动学，以适应可能存在接触漂移和雷达失效的场景。我们通过在多样环境的真实数据集上与其他芯片雷达里程计算法进行比较来评估所提方法，同时这些数据集将向机器人学界公开。https://github.com/SangwooJung98/Co-RaL-Dataset