Robust Localization, Mapping, and Navigation for Quadruped Robots

TL;DR

This paper introduces a robust localization, mapping, and navigation system for low-cost quadruped robots using contact-aided kinematic, visual-inertial odometry, and depth-stabilized vision, demonstrated in simulation and real-world tests.

Contribution

It presents a novel integration of sensors and methods to achieve accurate and robust navigation for low-cost quadruped robots, advancing practical deployment.

Findings

Accurate 2D environment mapping achieved

Robust self-localization demonstrated in real-world tests

System effective across multiple quadruped platforms

Abstract

Quadruped robots are currently a widespread platform for robotics research, thanks to powerful Reinforcement Learning controllers and the availability of cheap and robust commercial platforms. However, to broaden the adoption of the technology in the real world, we require robust navigation stacks relying only on low-cost sensors such as depth cameras. This paper presents a first step towards a robust localization, mapping, and navigation system for low-cost quadruped robots. In pursuit of this objective we combine contact-aided kinematic, visual-inertial odometry, and depth-stabilized vision, enhancing stability and accuracy of the system. Our results in simulation and two different real-world quadruped platforms show that our system can generate an accurate 2D map of the environment, robustly localize itself, and navigate autonomously. Furthermore, we present in-depth ablation studies of the important components of the system and their impact on localization accuracy. Videos, code, and additional experiments can be found on the project website: https://sites.google.com/view/low-cost-quadruped-slam