Robust Localization, Mapping, and Navigation for Quadruped Robots

2025-05-04

AI Summary

To address the challenge of robust localization, mapping, and navigation for low-cost quadrupedal robots in GPS-denied environments, this paper proposes a tightly coupled SLAM framework relying solely on low-cost sensors—primarily a depth camera. The method innovatively integrates contact-aided kinematic modeling, visual-inertial odometry (VIO), and depth-image stabilization into a unified multi-sensor estimator, augmented by a graph-optimization-based 2D occupancy grid mapping module. This tight coupling significantly enhances localization stability and mapping accuracy on complex, unstructured terrain. Extensive evaluations on simulation and real-world platforms—including ANYmal-C and Go1—demonstrate centimeter-level pose estimation accuracy, real-time 2D mapping, and fully autonomous navigation. Ablation studies quantitatively validate the individual contributions and synergistic gains of each component.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Computer Vision: Low Level & Physics-based Vision
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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

Problem

• Robust navigation for low-cost quadruped robots
• Accurate localization and mapping with cheap sensors
• Enhancing stability using multi-sensor fusion techniques

Innovation

• Combines contact-aided kinematic and visual-inertial odometry
• Uses depth-stabilized vision for enhanced accuracy
• Relies on low-cost sensors like depth cameras