论文标题
RGB-L：使用基于激光雷达的密度深度图增强间接视觉猛击
RGB-L: Enhancing Indirect Visual SLAM using LiDAR-based Dense Depth Maps
论文作者
论文摘要
在本文中，我们提出了一种通过在RGB-D模式下构建3D激光雷达深度测量到现有的ORB-SLAM3中的新方法。我们提出并比较了深度图生成的两种方法：传统的计算机视觉方法，即相反的扩张操作以及一种有监督的基于深度学习的方法。我们通过添加直接读取LiDar Point Clouds的所谓RGB-L（LIDAR）模式，将前者直接集成到ORB-SLAM3框架中。提出的方法在KITTI ODOMETIRY数据集上进行了评估，并相互比较和标准的ORB-SLAM 3立体声方法。我们证明，根据环境，可以实现轨迹准确性和鲁棒性的优势。此外，我们证明了与立体声模式相比，ORB-SLAM3算法的运行时间可以降低40％以上。 ORB-SLAM3 RGB-L模式的相关代码将在https://github.com/tumftm/orb slam3 rgbl下作为开源软件提供。
In this paper, we present a novel method for integrating 3D LiDAR depth measurements into the existing ORB-SLAM3 by building upon the RGB-D mode. We propose and compare two methods of depth map generation: conventional computer vision methods, namely an inverse dilation operation, and a supervised deep learning-based approach. We integrate the former directly into the ORB-SLAM3 framework by adding a so-called RGB-L (LiDAR) mode that directly reads LiDAR point clouds. The proposed methods are evaluated on the KITTI Odometry dataset and compared to each other and the standard ORB-SLAM3 stereo method. We demonstrate that, depending on the environment, advantages in trajectory accuracy and robustness can be achieved. Furthermore, we demonstrate that the runtime of the ORB-SLAM3 algorithm can be reduced by more than 40 % compared to the stereo mode. The related code for the ORB-SLAM3 RGB-L mode will be available as open-source software under https://github.com/TUMFTM/ORB SLAM3 RGBL.
