  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.
