Curved Surface Patches for Rough Terrain Perception

TL;DR

This paper develops 3D perception algorithms using curved surface patches to identify and map potential contact areas for legged robots navigating rough outdoor terrain, enabling reliable foothold detection in unstructured environments.

Contribution

It introduces a novel patch parameterization and fitting algorithm, along with a real-time patch-based mapping system for terrain perception in robotic foothold planning.

Findings

Successful real-time patch mapping on a mini-biped robot

Effective identification of contact surfaces on rocky terrain

Integration with dense range data for dynamic environment understanding

Abstract

Attaining animal-like legged locomotion on rough outdoor terrain with sparse foothold affordances -a primary use-case for legs vs other forms of locomotion- is a largely open problem. New advancements in control and perception have enabled bipeds to walk on flat and uneven indoor environments. But tasks that require reliable contact with unstructured world surfaces, for example walking on natural rocky terrain, need new perception and control algorithms. This thesis introduces 3D perception algorithms for contact tasks such as foot placement in rough terrain environments. We introduce a new method to identify and model potential contact areas between the robot's foot and a surface using a set of bounded curved patches. We present a patch parameterization model and an algorithm to fit and perceptually validate patches to 3D point samples. Having defined the environment representation using the patch model, we introduce a way to assemble patches into a spatial map. This map represents a sparse set of local areas potentially appropriate for contact between the robot and the surface. The process of creating such a map includes sparse seed point sampling, neighborhood searching, as well as patch fitting and validation. Various ways of sampling are introduced including a real time bio-inspired system for finding patches statistically similar to those that humans select while traversing rocky trails. These sparse patch algorithms are integrated with a dense volumetric fusion of range data from a moving depth camera, maintaining a dynamic patch map of relevant contact surfaces around a robot in real time. We integrate and test the algorithms as part of a real-time foothold perception system on a mini-biped robot, performing foot placements on rocks.