论文标题
DMLO：深度匹配的激光镜射仪
DMLO: Deep Matching LiDAR Odometry
论文作者
论文摘要
对于机器人技术，自动驾驶等各个领域，激光射道是一项基本任务。这个问题很困难，因为它要求系统在嘈杂的现实世界数据中运行高度可靠。现有方法主要是局部迭代方法。基于特征的全局注册方法不是首选的，因为在非均匀和稀疏的LiDAR数据中提取准确的匹配对仍然具有挑战性。在本文中，我们提出了深层匹配的激光镜（DMLO），这是一种基于学习的新型框架，它使特征匹配方法适用于LIDAR ODMOTIENTRY任务。与许多基于学习的方法不同，DMLO明确地在框架中执行了几何约束。具体而言，DMLO将6-DOF姿势估计分为两个部分，这是一个基于学习的匹配网络，该网络通过单数值分解（SVD）提供了两次扫描和刚体转换估计之间的准确对应关系。对实际数据集Kitti和Argoverse的全面实验结果表明，我们的DMLO极大地优于现有的基于学习的方法，并且与基于最新几何的方法相媲美。
LiDAR odometry is a fundamental task for various areas such as robotics, autonomous driving. This problem is difficult since it requires the systems to be highly robust running in noisy real-world data. Existing methods are mostly local iterative methods. Feature-based global registration methods are not preferred since extracting accurate matching pairs in the nonuniform and sparse LiDAR data remains challenging. In this paper, we present Deep Matching LiDAR Odometry (DMLO), a novel learning-based framework which makes the feature matching method applicable to LiDAR odometry task. Unlike many recent learning-based methods, DMLO explicitly enforces geometry constraints in the framework. Specifically, DMLO decomposes the 6-DoF pose estimation into two parts, a learning-based matching network which provides accurate correspondences between two scans and rigid transformation estimation with a close-formed solution by Singular Value Decomposition (SVD). Comprehensive experimental results on real-world datasets KITTI and Argoverse demonstrate that our DMLO dramatically outperforms existing learning-based methods and comparable with the state-of-the-art geometry based approaches.
