低成本四足机器人实现精准定位建图与自主导航
Robust Localization, Mapping, and Navigation for Quadruped Robots
- 融合接触反馈、视觉惯性与深度稳定视觉，提升系统稳定性
- 仿真与真实平台测试均实现2D环境地图构建与自主导航
- 适合低算力、低成本机器人开发者参考
四足机器人因强化学习控制器和廉价可靠的商业平台而广泛用于机器人研究。为推动该技术在现实世界的应用，需依赖低成本传感器（如深度相机）的鲁棒导航系统。本文提出首个面向低成本四足机器人的鲁棒定位、建图与导航系统。通过结合接触辅助运动学、视觉惯性里程计与深度稳定视觉，显著提升了系统的稳定性与精度。仿真及两种不同真实四足平台的实验表明，本系统可生成精确的2D环境地图，实现稳健自定位，并完成自主导航。此外，我们进行了深入消融实验，分析各模块对定位精度的影响。视频、代码及更多实验详见项目网站：https://sites.google.com/view/low-cost-quadruped-slam
原文摘要 · Abstract (English)
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
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