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:本文提出了一种具有低漂移特性的新型视觉-LiDAR里程计和建图方法。该方法基于两种流行的方法,即ORB-SLAM和A-LOAM,结合单目比例校正和视觉辅助LiDAR运动补偿修改。尺度校正器通过利用离群值拒绝过程计算由三角测量恢复的图像关键点深度和LiDAR提供深度之间的比例,以提高精度。关于LiDAR运动补偿,视觉里程计方法为更好的性能提供LiDAR运动的初始猜测。这种方法不仅适用于高分辨率LiDAR,而且还可以适应低分辨率LiDAR。为了评估所提出的SLAM系统的鲁棒性和精度,我们对KITTI Odometry和S3E数据集进行了实验。实验结果表明,我们的方法明显优于独立的ORB-SLAM2和A-LOAM。此外,关于具有比例校正的视觉里程计的准确性,我们的方法的表现类似于立体模式ORB-SLAM2。