CamVox: A Low-cost and Accurate Lidar-assisted Visual SLAM System
This work addresses the problem of improving SLAM accuracy and efficiency for researchers and developers working with low-cost lidar-camera systems, offering an incremental improvement.
The paper proposes CamVox, a low-cost lidar-assisted visual SLAM system that integrates Livox lidars with ORB-SLAM2. It introduces an automatic lidar-camera calibration method for uncontrolled scenes and leverages the lidar's long depth detection for more efficient mapping.
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.