Pedestrian Gait-Enhanced LINS: A Robust, IMU-Centric LiDAR-Inertial Navigation System for Pedestrian

• Published in: IEEE sensors journal Vol. 25; no. 7; pp. 11348 - 11356
• Main Authors: Liao, Zongbo, Zhang, Xuanxuan, Zhang, Tianxiang, Li, You
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accelerometers; Accuracy; Degeneracy factor; Feature extraction; Gait; gait-enhanced; Inertial navigation; Information systems; Kalman filters; Laser radar; Lidar; LiDAR-inertial navigation system (LINS); Navigation systems; Observability (systems); Odometry; pedestrian; Pedestrians; Point cloud compression; Robustness; Simultaneous localization and mapping; State estimation
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2024.3523131

We propose a robust, pedestrian gait-enhanced, IMU-centric, LiDAR-inertial navigation system (P-LINS) for pedestrian applications. The system makes full use of the information of the IMU in state estimation and LiDAR processing. To improve the system's performance further, we integrate the physiological characteristics of pedestrian movements, applying constraints on velocity estimates. Additionally, we propose a novel LiDAR degeneracy factor to assess the observability of LiDAR rotation and translation, dynamically adjusting the LiDAR's weight in the error state Kalman filter (ESKF) based on the degeneracy factor. Through experiments in structured scenarios and unstructured scenarios with LiDAR degeneracy, the robustness and precision of P-LINS are thoroughly validated. P-LINS performs comparably in structured scenarios to the SOTA LiDAR-inertial navigation systems (LINSs). In unstructured scenarios with LiDAR degeneracy, our method shows better robustness and accuracy. We open-source our codes combined with the dataset at https://github.com/piapi/P-LINS .