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.

该论文提出一种新颖的视觉 - LiDAR 里程计和建图方法，该方法基于 ORB-SLAM 和 A-LOAM 两种常用方法，并采用单目尺度校正和视觉辅助 LiDAR 运动补偿修改，其显著优于 ORB-SLAM2 和 A-LOAM，而其视觉里程计的准确性类似于 ORB-SLAM2 的立体模式。

单目尺度校正与运动补偿的视觉 - LiDAR 里程计与建图

Visual-LiDAR Odometry and Mapping with Monocular Scale Correction and  Motion Compensation

Most learning-based methods estimate ego-motion by utilizing visual sensors,
which suffer from dramatic lighting variations and textureless scenarios. In
this paper, we incorporate sparse but accurate depth measurements obtained from
lidars to overcome the limitation of visual methods. To this end, we design a
self-supervised visual-lidar odometry (Self-VLO) framework. It takes both
monocular images and sparse depth maps projected from 3D lidar points as input,
and produces pose and depth estimations in an end-to-end learning manner,
without using any ground truth labels. To effectively fuse two modalities, we
design a two-pathway encoder to extract features from visual and depth images
and fuse the encoded features with those in decoders at multiple scales by our
fusion module. We also adopt a siamese architecture and design an adaptively
weighted flip consistency loss to facilitate the self-supervised learning of
our VLO. Experiments on the KITTI odometry benchmark show that the proposed
approach outperforms all self-supervised visual or lidar odometries. It also
performs better than fully supervised VOs, demonstrating the power of fusion.

利用激光雷达传感器获取的稀疏但准确的深度测量，设计了一个自监督视觉 - 激光雷达里程计 (Self-VLO) 框架，该框架通过两个路径编码器提取视觉和深度图像中的特征，并将这些特征与多尺度解码器中的特征通过融合模块融合起来，从而在端到端学习的方式下产生姿态和深度估计。实验结果表明，该方法优于所有自监督视觉或激光雷达里程计，并且优于全监督视觉里程表，证明了融合的强大表现能力。