融合激光雷达与视觉信息，实现无需惯性单元的高精度三维重建。
LiVisSfM: Accurate and Robust Structure-from-Motion with LiDAR and Visual Cues
- 基于点到高斯残差优化激光雷达帧与体素地图配准
- 在KITTI和自采数据集上实现更精准的位姿估计与稠密点云重建
- 适合需要高鲁棒性三维重建的自动驾驶与机器人领域
本文提出一种名为LiVisSfM的精确且鲁棒的结构光运动（SfM）框架，该系统完全融合激光雷达与视觉信息。不同于依赖惯性测量单元（IMU）进行激光雷达-惯性里程计（LIO）或激光雷达-惯性-视觉里程计（LIVO）的方法，本工作创新性地采用点到高斯残差度量实现激光雷达帧与激光雷达体素地图的配准，并结合激光雷达-视觉束调整（BA）与显式回环闭合，在整体优化中实现不依赖IMU的高精度激光雷达位姿估计。此外，提出一种增量式体素更新策略，以提升激光雷达帧配准与激光雷达-视觉BA优化过程中的地图更新效率。实验表明，该框架在公开的KITTI基准数据集及多种自采集数据集上，均显著优于当前最先进的LIO与LIVO方法，在激光雷达位姿恢复与稠密点云重建方面表现更优。
原文摘要 · Abstract (English)
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.
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