CamVox: A Low-cost and Accurate Lidar-assited Visual SLAM System

Combining lidar in camera-based simultaneous localization and mapping (SLAM) is an effective method in improving overall accuracy, especially at outdoor large scale scenes. 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 unique scan pattern 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 accurate mapping. Comparison of CamVox with visual SLAM (VINS-mono) and lidar SLAM (livox_horizon_loam) are evaluated on the same dataset to demonstrate the performance. We open sourced our hardware, code and dataset on Github. (https://github.com/ISEE-Technology/CamVox)

This contains our dataset in SUSTech campus with loop closure (CamVox.bag) and the Lidar-camera Synchronization ARM(stm32) code (Synchronization.rar ).