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.

中文翻译：

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CamVox：一种低成本，精确的激光雷达辅助Visual SLAM系统

在基于相机的同时定位和制图（SLAM）中结合激光雷达是一种提高整体精度的有效方法，尤其是在大规模室外场景下。低成本激光雷达（例如Livox激光雷达）的最新发展使我们能够探索具有更低预算和更高性能的SLAM系统。在本文中，我们通过探索Livox激光雷达的独特功能，将Livox激光雷达应用于视觉SLAM（ORB-SLAM2），提出了CamVox。基于Livox激光雷达的非重复性，我们提出了一种自动激光雷达摄像机标定方法，该方法可在不受控制的场景中使用。长的深度检测范围也有利于更有效的映射。在同一数据集上评估了CamVox与视觉SLAM（VINS-mono）和激光雷达SLAM（LOAM）的比较，以证明其性能。我们在GitHub上开源了我们的硬件，代码和数据集。