Gaussian Process Distance Fields Obstacle and Ground Constraints for Safe Navigation

TL;DR

This paper introduces a 3D navigation method using Gaussian Process distance fields and a quadtree structure to enable safe, smooth paths for ground-based robots and humans in cluttered, uneven environments.

Contribution

It presents a novel scene representation and trajectory optimization technique that handles 3D obstacles and terrain for diverse ground-based systems.

Findings

Effective handling of uneven terrain, steps, and overhanging objects.

Safe and smooth navigation paths demonstrated on synthetic and real datasets.

Applicable to both wheeled and legged robots, as well as humans.

Abstract

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.