Environmental-structure-perception-based adaptive pose fusion method for LiDAR-visual-inertial odometry

Abstract

Light Detection and Ranging (LiDAR)-visual-inertial odometry can provide accurate poses for the localization of unmanned vehicles working in unknown environments in the absence of Global Positioning System (GPS). Since the quality of poses estimated by different sensors in environments with different structures fluctuates greatly, existing pose fusion models cannot guarantee stable performance of pose estimations in these environments, which brings great challenges to the pose fusion of LiDAR-visual-inertial odometry. This article proposes a novel environmental structure perception-based adaptive pose fusion method, which achieves the online optimization of the parameters in the pose fusion model of LiDAR-visual-inertial odometry by analyzing the complexity of environmental structure. Firstly, a novel quantitative perception method of environmental structure is proposed, and the visual bag-of-words vector and point cloud feature histogram are constructed to calculate the quantitative indicators describing the structural complexity of visual image and LiDAR point cloud of the surroundings, which can be used to predict and evaluate the pose quality from LiDAR/visual measurement models of poses. Then, based on the complexity of the environmental structure, two pose fusion strategies for two mainstream pose fusion models (Kalman filter and factor graph optimization) are proposed, which can adaptively fuse the poses estimated by LiDAR and vision online. Two state-of-the-art LiDAR-visual-inertial odometry systems are selected to deploy the proposed environmental structure perception-based adaptive pose fusion method, and extensive experiments are carried out on both open-source data sets and self-gathered data sets. The experimental results show that environmental structure perception-based adaptive pose fusion method can effectively perceive the changes in environmental structure and execute adaptive pose fusion, improving the accuracy of pose estimation of LiDAR-visual-inertial odometry in environments with changing structures.