Leg Exoskeleton Odometry using a Limited FOV Depth Sensor

TL;DR

This paper presents a novel odometry algorithm for leg exoskeletons that fuses proprioceptive data with limited FOV depth sensor point clouds, improving terrain perception in constrained environments.

Contribution

The paper introduces a new odometry method combining EKF and ICP tailored for exoskeletons with limited FOV sensors, addressing unique perception challenges.

Findings

Reduces odometry drift compared to baseline methods

Produces higher quality elevation maps in real-world tests

Outperforms traditional point cloud mapping approaches

Abstract

For leg exoskeletons to operate effectively in real-world environments, they must be able to perceive and understand the terrain around them. However, unlike other legged robots, exoskeletons face specific constraints on where depth sensors can be mounted due to the presence of a human user. These constraints lead to a limited Field Of View (FOV) and greater sensor motion, making odometry particularly challenging. To address this, we propose a novel odometry algorithm that integrates proprioceptive data from the exoskeleton with point clouds from a depth camera to produce accurate elevation maps despite these limitations. Our method builds on an extended Kalman filter (EKF) to fuse kinematic and inertial measurements, while incorporating a tailored iterative closest point (ICP) algorithm to register new point clouds with the elevation map. Experimental validation with a leg exoskeleton demonstrates that our approach reduces drift and enhances the quality of elevation maps compared to a purely proprioceptive baseline, while also outperforming a more traditional point cloud map-based variant.