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-assisted LiDAR motion compensation 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 motion compensation, 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：本文提出了一种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，通过引入单目尺度校正与视觉辅助激光雷达运动补偿改进。尺度校正器计算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，并采用离群值剔除流程提升精度。针对激光雷达运动补偿，视觉里程计方法为激光雷达运动提供初始估计以优化性能。该方法不仅适用于高分辨率激光雷达，亦可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数据集上开展了实验。实验结果表明，本方法显著优于独立的ORB-SLAM2与A-LOAM。此外，在带尺度校正的视觉里程计精度方面，本方法达到了与立体模式ORB-SLAM2相近的性能。