论文标题
UNPWC-SVDLO：在PointPWC上用于无监督的LIDAR ODOMETIRE的多SVD
UnPWC-SVDLO: Multi-SVD on PointPWC for Unsupervised Lidar Odometry
论文作者
论文摘要
高精度LiDAR ODOMETY是自动驾驶的重要组成部分。近年来，深度学习方法已被广泛用于LiDAR ODOMETY任务中，但是当前大多数方法仅提取点云的全局特征。以这种方式获得更详细的点级功能是不可能的。另外，仅使用完全连接的层来估计姿势。完全连接的层在分类任务中取得了明显的结果，但是姿势的变化是连续的而不是离散过程，只能通过使用完全连接的层才能获得高精度姿势估计。我们的方法避免了上述问题。我们将PointPWC用作骨干网络。 PointPWC最初用于场景流估计。场景流估计任务与LiDAR ODOMETY具有很强的相关性。 traget点云可以通过添加场景流和源点云来获得。我们可以通过SVD求解的ICP算法直接实现姿势，并且不再使用完全连接的层。 PointPWC从具有不同采样级别的点云中提取点级特征，从而解决了过于粗糙的特征提取的问题。我们在Kitti，Ford Campus Vision和LiDar数据酶和Apollo-Southbay数据集上进行实验。我们的结果与最先进的无监督的深度学习方法自我voxelo相媲美。
High-precision lidar odomety is an essential part of autonomous driving. In recent years, deep learning methods have been widely used in lidar odomety tasks, but most of the current methods only extract the global features of the point clouds. It is impossible to obtain more detailed point-level features in this way. In addition, only the fully connected layer is used to estimate the pose. The fully connected layer has achieved obvious results in the classification task, but the changes in pose are a continuous rather than discrete process, high-precision pose estimation can not be obtained only by using the fully connected layer. Our method avoids problems mentioned above. We use PointPWC as our backbone network. PointPWC is originally used for scene flow estimation. The scene flow estimation task has a strong correlation with lidar odomety. Traget point clouds can be obtained by adding the scene flow and source point clouds. We can achieve the pose directly through ICP algorithm solved by SVD, and the fully connected layer is no longer used. PointPWC extracts point-level features from point clouds with different sampling levels, which solves the problem of too rough feature extraction. We conduct experiments on KITTI, Ford Campus Vision and Lidar DataSe and Apollo-SouthBay Dataset. Our result is comparable with the state-of-the-art unsupervised deep learing method SelfVoxeLO.
