论文标题
Camvox：低成本，准确的激光镜辅助视觉大满贯系统
CamVox: A Low-cost and Accurate Lidar-assisted Visual SLAM System
论文作者
论文摘要
在基于摄像机的同时定位和映射（SLAM）中结合LIDAR是提高整体准确性的有效方法，尤其是在大规模的室外情况下。低成本激光雷达（例如Livox LiDAR）的最新发展使我们能够探索预算较低和较高性能的SLAM系统。在本文中，我们通过探索LiDars的独特功能来将Livox激光雷达调整为Visual Slam（Orb-Slam2）提出Camvox。基于Livox激光雷达的非重复性性质，我们提出了一种自动激光镜相机校准方法，该方法将在不受控制的场景中起作用。长深度检测范围也使更有效的映射受益。在同一数据集上评估了Camvox与视觉大满贯（VIN-MONO）和LIDAR SLAM（壤土）的比较，以证明性能。我们在GitHub上开了高处的硬件，代码和数据集。
Combining lidar in camera-based simultaneous localization and mapping (SLAM) is an effective method in improving overall accuracy, especially at a large scale outdoor scenario. Recent development of low-cost lidars (e.g. Livox lidar) enable us to explore such SLAM systems with lower budget and higher performance. In this paper we propose CamVox by adapting Livox lidars into visual SLAM (ORB-SLAM2) by exploring the lidars' unique features. Based on the non-repeating nature of Livox lidars, we propose an automatic lidar-camera calibration method that will work in uncontrolled scenes. The long depth detection range also benefit a more efficient mapping. Comparison of CamVox with visual SLAM (VINS-mono) and lidar SLAM (LOAM) are evaluated on the same dataset to demonstrate the performance. We open sourced our hardware, code and dataset on GitHub.
