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

Yujian Qiu, Yuqiu Mu, Wen Yang, Hao Zhu

TL;DR

The paper tackles accurate localization for quadrupedal robots in long, feature-sparse corridors where LiDAR alone offers weak geometric constraints. It introduces AIMS, an adaptive multisensor fusion framework based on an error-state Kalman filter that fuses IMU-based prediction with LiDAR and leg odometry corrections, augmented by a degeneracy-aware reliability assessment to reweight sensor contributions. A key contribution is a degeneracy severity index that combines LiDAR observability and IMU-LiDAR consistency to modulate information injection, improving robustness in perceptual degeneration. Experimental results in indoor long corridors and garage corridors show that AIMS reduces degeneration and endpoint drift compared with state-of-the-art baselines, indicating strong practical value for quadrupedal inspection in degenerate environments. Overall, the method enhances reliability and accuracy of localization under perceptual degradation, enabling safer autonomous navigation in challenging industrial settings.

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.

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

TL;DR

The paper tackles accurate localization for quadrupedal robots in long, feature-sparse corridors where LiDAR alone offers weak geometric constraints. It introduces AIMS, an adaptive multisensor fusion framework based on an error-state Kalman filter that fuses IMU-based prediction with LiDAR and leg odometry corrections, augmented by a degeneracy-aware reliability assessment to reweight sensor contributions. A key contribution is a degeneracy severity index that combines LiDAR observability and IMU-LiDAR consistency to modulate information injection, improving robustness in perceptual degeneration. Experimental results in indoor long corridors and garage corridors show that AIMS reduces degeneration and endpoint drift compared with state-of-the-art baselines, indicating strong practical value for quadrupedal inspection in degenerate environments. Overall, the method enhances reliability and accuracy of localization under perceptual degradation, enabling safer autonomous navigation in challenging industrial settings.

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.
Paper Structure (12 sections, 14 equations, 7 figures, 2 tables)

This paper contains 12 sections, 14 equations, 7 figures, 2 tables.

Table of Contents

  1. Introduction
  2. The Proposed Method
  3. System Overview
  4. IMU-Based State Prediction
  5. LiDAR and Leg Odometry Measurement Models
  6. Degeneracy-Aware Reliability Assessment
  7. Adaptive Multisensor Fusion
  8. Experiments
  9. Experimental Platform and Evaluation Metrics
  10. Indoor Long Corridor Experiment
  11. Indoor Garage Corridor Experiment
  12. Conclusion

Figures (7)

  • Figure 1: Framework of the proposed AIMS.
  • Figure 2: Diagram of Coordinate Frames.
  • Figure 3: The 3D point cloud map constructed by the proposed method in the long corridor experiment.
  • Figure 4: Trajectories estimated by legged odometry and other methods in the long corridor experiment.
  • Figure 5: 3D point cloud maps constructed by LOAM, FAST-LIO2, LEGO-LOAM, and AIMS in the long corridor experiment.
  • ...and 2 more figures