LiVisSfM: Accurate and Robust Structure-from-Motion with LiDAR and Visual Cues
This addresses the need for more reliable 3D reconstruction in robotics and autonomous systems, offering an incremental improvement over existing LiDAR-inertial methods.
The paper tackles the problem of accurate and robust Structure-from-Motion (SfM) by proposing LiVisSfM, a pipeline that combines LiDAR and visual cues without relying on IMU, achieving superior LiDAR pose recovery and dense point cloud reconstruction on benchmarks like KITTI.
This paper presents an accurate and robust Structure-from-Motion (SfM) pipeline named LiVisSfM, which is an SfM-based reconstruction system that fully combines LiDAR and visual cues. Unlike most existing LiDAR-inertial odometry (LIO) and LiDAR-inertial-visual odometry (LIVO) methods relying heavily on LiDAR registration coupled with Inertial Measurement Unit (IMU), we propose a LiDAR-visual SfM method which innovatively carries out LiDAR frame registration to LiDAR voxel map in a Point-to-Gaussian residual metrics, combined with a LiDAR-visual BA and explicit loop closure in a bundle optimization way to achieve accurate and robust LiDAR pose estimation without dependence on IMU incorporation. Besides, we propose an incremental voxel updating strategy for efficient voxel map updating during the process of LiDAR frame registration and LiDAR-visual BA optimization. Experiments demonstrate the superior effectiveness of our LiVisSfM framework over state-of-the-art LIO and LIVO works on more accurate and robust LiDAR pose recovery and dense point cloud reconstruction of both public KITTI benchmark and a variety of self-captured dataset.