论文标题
pwclo-net：使用层次嵌入掩码优化的3D点云中的深色激光射击仪
PWCLO-Net: Deep LiDAR Odometry in 3D Point Clouds Using Hierarchical Embedding Mask Optimization
论文作者
论文摘要
本文提出了一种新型的3D点云学习模型，该模型使用层次嵌入掩码优化，称为pwclo-net，在本文中提出了层次嵌入。在此模型中，LIDAR ODMOTIENTRY任务的金字塔，翘曲和成本量（PWC）结构是为了在粗到精细的方法层次上构建的估计姿势。建立了一个细心的成本量，以将两个点云关联并获得嵌入运动模式。然后，提出了一种可训练的嵌入面膜，以权衡所有点的局部运动模式，以回归整体姿势和过滤器离群值。估计的电流姿势用于扭曲第一个点云，以弥合到与第二点云的距离，然后构建剩余运动的成本体积。同时，嵌入面膜从层次上从粗糙到细小进行优化，以获取更准确的过滤信息以进行姿势细化。可训练的姿势扭曲过程被迭代地用于使姿势估计更适合异常值。在Kitti Odometry数据集上证明了我们的LiDAR进程模型的出色性能和有效性。我们的方法优于所有基于学习的方法，并且在大多数Kitti Odometry Dataset的序列上都胜过基于几何的方法，质量优化。
A novel 3D point cloud learning model for deep LiDAR odometry, named PWCLO-Net, using hierarchical embedding mask optimization is proposed in this paper. In this model, the Pyramid, Warping, and Cost volume (PWC) structure for the LiDAR odometry task is built to refine the estimated pose in a coarse-to-fine approach hierarchically. An attentive cost volume is built to associate two point clouds and obtain embedding motion patterns. Then, a novel trainable embedding mask is proposed to weigh the local motion patterns of all points to regress the overall pose and filter outlier points. The estimated current pose is used to warp the first point cloud to bridge the distance to the second point cloud, and then the cost volume of the residual motion is built. At the same time, the embedding mask is optimized hierarchically from coarse to fine to obtain more accurate filtering information for pose refinement. The trainable pose warp-refinement process is iteratively used to make the pose estimation more robust for outliers. The superior performance and effectiveness of our LiDAR odometry model are demonstrated on KITTI odometry dataset. Our method outperforms all recent learning-based methods and outperforms the geometry-based approach, LOAM with mapping optimization, on most sequences of KITTI odometry dataset.Our source codes will be released on https://github.com/IRMVLab/PWCLONet.
