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.

翻译：本文提出一种具有低漂移特性的新型视觉-激光雷达里程计与建图方法。该方法基于ORB-SLAM和A-LOAM两种主流框架，引入单目尺度校正与视觉引导的激光雷达位姿初始化改进。尺度校正器通过异常值剔除过程提升精度，计算由三角测量恢复的图像关键点深度与激光雷达提供深度之间的比例关系。针对激光雷达位姿初始化，视觉里程计方法为激光雷达运动提供初始估计值以提升性能。该方法不仅适用于高分辨率激光雷达，也能适配低分辨率激光雷达。为评估所提SLAM系统的鲁棒性与精度，我们在KITTI里程计和S3E数据集上开展实验。实验结果表明，该方法显著优于单独使用ORB-SLAM2与A-LOAM。此外，在具备尺度校正的视觉里程计精度方面，本方法与立体模式的ORB-SLAM2表现相当。