论文标题
Pointloc：激光点云本地化的深度姿势回归器
PointLoc: Deep Pose Regressor for LiDAR Point Cloud Localization
论文作者
论文摘要
在本文中，我们提出了一种新型的基于端到端学习的LiDAR重新定位框架，称为PointLoc，该框架仅使用单点云作为输入直接构成6DOF，而无需预先构建的地图。与基于RGB图像的重新定位相比，激光镜头框架可以提供有关场景的丰富而强大的几何信息。但是，激光点云是无序的和非结构化的，因此很难为此任务应用传统的深度学习回归模型。我们通过提出一种具有自我注意力的新型PointNet风格的体系结构来解决这个问题，以有效估计来自360°LIDAR输入框架的6-DOF姿势。最近发布的挑战性的牛津雷达雷达Robotcar数据集和现实世界机器人实验的扩展实验表明，提出的Method可以实现准确的重新定位性能。
In this paper, we present a novel end-to-end learning-based LiDAR relocalization framework, termed PointLoc, which infers 6-DoF poses directly using only a single point cloud as input, without requiring a pre-built map. Compared to RGB image-based relocalization, LiDAR frames can provide rich and robust geometric information about a scene. However, LiDAR point clouds are unordered and unstructured making it difficult to apply traditional deep learning regression models for this task. We address this issue by proposing a novel PointNet-style architecture with self-attention to efficiently estimate 6-DoF poses from 360° LiDAR input frames.Extensive experiments on recently released challenging Oxford Radar RobotCar dataset and real-world robot experiments demonstrate that the proposedmethod can achieve accurate relocalization performance.
