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

2026-01-04

arXiv.org

AI Summary

This work addresses the degradation of localization accuracy in legged robots operating in geometrically sparse environments—such as narrow tunnels—where LiDAR constraints are insufficient. To mitigate this issue, the authors propose an adaptive tightly coupled fusion framework that integrates LiDAR, IMU, and legged odometry within an error-state Kalman filter. The method incorporates an online observability-aware mechanism that dynamically evaluates environmental degeneracy and adaptively adjusts the observation noise covariance of each sensor accordingly. Experimental results demonstrate that, in degenerate scenarios like long corridors, the proposed approach significantly outperforms existing methods, achieving notably improved localization accuracy and robustness.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Calibration & Uncertainty Quantification
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• quadrupedal robot
• localization
• degenerate environments
• LiDAR
• sensor fusion

Innovation

• adaptive sensor fusion
• degenerate environment localization
• LiDAR-IMU-leg odometry
• error-state Kalman filter
• quadrupedal robot