Accurate Pose Prediction on Signed Distance Fields for Mobile Ground Robots in Rough Terrain

TL;DR

This paper introduces a new iterative geometric method that leverages signed distance fields to accurately predict the 3D pose of mobile ground robots with active flippers in rough terrain, enabling improved online planning.

Contribution

The paper presents a novel approach using signed distance fields for high-accuracy, online 3D pose prediction of robots with active flippers on uneven terrain, outperforming previous heightmap-based methods.

Findings

Predicts robot position with 3.11 cm accuracy

Predicts orientation with 3.91° accuracy

Outperforms recent heightmap-based approaches

Abstract

Autonomous locomotion for mobile ground robots in unstructured environments such as waypoint navigation or flipper control requires a sufficiently accurate prediction of the robot-terrain interaction. Heuristics like occupancy grids or traversability maps are widely used but limit actions available to robots with active flippers as joint positions are not taken into account. We present a novel iterative geometric method to predict the 3D pose of mobile ground robots with active flippers on uneven ground with high accuracy and online planning capabilities. This is achieved by utilizing the ability of signed distance fields to represent surfaces with sub-voxel accuracy. The effectiveness of the presented approach is demonstrated on two different tracked robots in simulation and on a real platform. Compared to a tracking system as ground truth, our method predicts the robot position and orientation with an average accuracy of 3.11 cm and 3.91{\deg}, outperforming a recent heightmap-based approach. The implementation is made available as an open-source ROS package.