Visual-LiDAR Odometry and Mapping with Monocular Scale Correction and Visual Bootstrapping
This work addresses drift reduction in SLAM systems for robotics and autonomous vehicles, representing an incremental improvement through hybrid integration of existing methods.
The paper tackles the problem of drift in visual-LiDAR odometry and mapping by integrating ORB-SLAM and A-LOAM with monocular scale correction and visual bootstrapping, achieving significant performance improvements over standalone methods and matching stereo-mode ORB-SLAM2 in accuracy.
This paper presents a novel visual-LiDAR odometry and mapping method with low-drift characteristics. The proposed method is based on two popular approaches, ORB-SLAM and A-LOAM, with monocular scale correction and visual-bootstrapped LiDAR poses initialization modifications. The scale corrector calculates the proportion between the depth of image keypoints recovered by triangulation and that provided by LiDAR, using an outlier rejection process for accuracy improvement. Concerning LiDAR poses initialization, the visual odometry approach gives the initial guesses of LiDAR motions for better performance. This methodology is not only applicable to high-resolution LiDAR but can also adapt to low-resolution LiDAR. To evaluate the proposed SLAM system's robustness and accuracy, we conducted experiments on the KITTI Odometry and S3E datasets. Experimental results illustrate that our method significantly outperforms standalone ORB-SLAM2 and A-LOAM. Furthermore, regarding the accuracy of visual odometry with scale correction, our method performs similarly to the stereo-mode ORB-SLAM2.