UnPWC-SVDLO: Multi-SVD on PointPWC for Unsupervised Lidar Odometry
This addresses lidar odometry for autonomous driving, but it is incremental as it builds on existing methods like PointPWC and SVD.
The paper tackles the problem of high-precision lidar odometry for autonomous driving by proposing a method that uses PointPWC for point-level feature extraction and SVD-based ICP for pose estimation, achieving results comparable to the state-of-the-art unsupervised method SelfVoxeLO on datasets like KITTI.
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.