AIMS: An Adaptive Integration of Multi-Sensor Measurements for Quadrupedal Robot Localization

TL;DR

This paper introduces AIMS, an adaptive sensor fusion method combining LiDAR, IMU, and leg odometry within a Kalman filter for accurate, robust localization of quadrupedal robots in challenging tunnel-like environments.

Contribution

It presents a novel adaptive sensor fusion framework that dynamically adjusts measurement noise covariance, enhancing localization accuracy in degenerate environments.

Findings

Improved localization accuracy over existing methods.

Enhanced robustness in narrow tunnel environments.

Effective online degeneracy-aware measurement adjustment.

Abstract

This paper addresses the problem of accurate localization for quadrupedal robots operating in narrow tunnel-like environments. Due to the long and homogeneous characteristics of such scenarios, LiDAR measurements often provide weak geometric constraints, making traditional sensor fusion methods susceptible to accumulated motion estimation errors. To address these challenges, we propose AIMS, an adaptive LiDAR-IMU-leg odometry fusion method for robust quadrupedal robot localization in degenerate environments. The proposed method is formulated within an error-state Kalman filtering framework, where LiDAR and leg odometry measurements are integrated with IMU-based state prediction, and measurement noise covariance matrices are adaptively adjusted based on online degeneracy-aware reliability assessment. Experimental results obtained in narrow corridor environments demonstrate that the proposed method improves localization accuracy and robustness compared with state-of-the-art approaches.