A lightweight enhanced LiDAR-inertial-visual odometry system

doi:10.13474/j.cnki.11-2246.2025.0913

Abstract

Abstract: Lightweight intelligent tracking for enhanced LiDAR-IMU-visual odometry(LIVO)has broad applications in mobile robotics and autonomous driving.This paper proposes LITE-LIVO,a lightweight and enhanced LIVO system built upon FAST-LIVO,integrating LiDAR,IMU and vision sensors for real-time pose estimation and high-precision mapping.To enhance robustness under dynamic lighting,the system employs a deep learning-based feature extraction and sparse optical flow tracking,fusing visual and LiDAR data via Kalman filtering with visual residuals.A tightly coupled visual-IMU odometry (VIO)subsystem filters high-quality visual features from LiDAR point clouds and optimizes visual map management.Experimental results on public datasets and real-world scenarios demonstrate superior performance,especially in complex and degraded environments.This study advances multi-source data fusion techniques,improving localization accuracy and expanding application domains for mobile robots.

Key words: LiDAR-inertial-visual odometry, deep learning, optical flow tracking, Kalman filter

CLC Number:

P237

YANG Yanguang, QIAN Jianguo, YU Bin, GUO Jie, JIAO Yang. A lightweight enhanced LiDAR-inertial-visual odometry system[J]. Bulletin of Surveying and Mapping, 2025, 0(9): 78-83,104.

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