Traversability analysis with vision and terrain probing for safe legged robot navigation

TL;DR

This paper presents a novel traversability analysis framework for legged robots that combines vision, terrain probing, and collapsibility metrics to enhance safety in navigating unpredictable rough terrains.

Contribution

It introduces a new framework integrating probing strategies and collapsibility analysis into traversability assessment for improved outdoor navigation safety.

Findings

Effective in identifying collapsible terrain regions

Improves navigation safety in soft and unpredictable terrains

Validated on quadrupedal robot in simulation and real-world

Abstract

Inspired by human behavior when traveling over unknown terrain, this study proposes the use of probing strategies and integrates them into a traversability analysis framework to address safe navigation on unknown rough terrain. Our framework integrates collapsibility information into our existing traversability analysis, as vision and geometric information alone could be misled by unpredictable non-rigid terrains such as soft soil, bush area, or water puddles. With the new traversability analysis framework, our robot has a more comprehensive assessment of unpredictable terrain, which is critical for its safety in outdoor environments. The pipeline first identifies the terrain's geometric and semantic properties using an RGB-D camera and desired probing locations on questionable terrains. These regions are probed using a force sensor to determine the risk of terrain collapsing when the robot steps over it. This risk is formulated as a collapsibility metric, which estimates an unpredictable region's ground collapsibility. Thereafter, the collapsibility metric, together with geometric and semantic spatial data, is combined and analyzed to produce global and local traversability grid maps. These traversability grid maps tell the robot whether it is safe to step over different regions of the map. The grid maps are then utilized to generate optimal paths for the robot to safely navigate to its goal. Our approach has been successfully verified on a quadrupedal robot in both simulation and real-world experiments.