用高斯过程构建3D障碍物与地面距离场，实现安全导航。
Gaussian Process Distance Fields Obstacle and Ground Constraints for Safe Navigation
- 基于点云构建双高斯过程距离场，分别约束障碍物与地面
- 支持不平地形、台阶和悬垂障碍，路径平滑且避障可靠
- 适合轮式、足式机器人及人机协同，适用于复杂环境
在杂乱环境中导航对移动系统而言极具挑战。现有针对地面机器人的方法多聚焦于小型轮式机器人，受限于无悬垂障碍且无法应对台阶或楼梯，问题本质上为二维。而足式机器人（或人类）的导航则需考虑额外维度。本文提出一种定制化的场景表示方法，并结合先进的轨迹优化算法，实现安全的三维导航。给定场景的3D点云及地面与非地面点的分割结果，我们构建两个高斯过程距离场，确保路径无碰撞并维持与地面的距离约束。通过创新地使用四叉树结构，构建自由空间的多分辨率地图及其连通性图，基于相关场景的二维投影实现高效建模。在合成与真实数据集上的评估表明，该方法能生成安全、平滑的路径，适用于各类地面移动系统。
原文摘要 · Abstract (English)
Navigating cluttered environments is a challenging task for any mobile system. Existing approaches for ground-based mobile systems primarily focus on small wheeled robots, which face minimal constraints with overhanging obstacles and cannot manage steps or stairs, making the problem effectively 2D. However, navigation for legged robots (or even humans) has to consider an extra dimension. This paper proposes a tailored scene representation coupled with an advanced trajectory optimisation algorithm to enable safe navigation. Our 3D navigation approach is suitable for any ground-based mobile robot, whether wheeled or legged, as well as for human assistance. Given a 3D point cloud of the scene and the segmentation of the ground and non-ground points, we formulate two Gaussian Process distance fields to ensure a collision-free path and maintain distance to the ground constraints. Our method adeptly handles uneven terrain, steps, and overhanging objects through an innovative use of a quadtree structure, constructing a multi-resolution map of the free space and its connectivity graph based on a 2D projection of the relevant scene. Evaluations with both synthetic and real-world datasets demonstrate that this approach provides safe and smooth paths, accommodating a wide range of ground-based mobile systems.
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