Vanishing Point Aided LiDAR-Visual-Inertial Estimator

Peng Wang, Zheng Fang, Shibo Zhao, Yongnan Chen, Ming Zhou, Shan An

Abstract: In this paper, we propose a vanishing point aided LiDAR-Visual-Inertial estimator to achieve real-time, low-drift and robust pose estimation. The proposed method is mainly composed of 3 sequential modules, namely IMU-aided vanishing point (VP) detection module, voxel-map based feature depth association module, and visual inertial fixed-lag smoother module. The IMU-aided VP detection module will detect feature points, line segments and vanishing points to establish robust correspondences in successive frames. In particular, we propose to use 1-line RANSAC method to provide stable VP hypotheses and polar grid to accelerate vanishing point hypothesis validation. After that, we propose a novel voxel-map based feature depth association method, to retrieve depth and assign depth to visual feature efficiently. Finally, the visual inertial fixed-lag smoother module is proposed to jointly minimize error terms. Experiments show that our method outperforms the state-of-the-art visual-inertial odometry and LiDAR-visual estimator in both indoor and outdoor environments.