EgoNN: Egocentric Neural Network for Point Cloud Based 6DoF Relocalization at the City Scale
Illustration of the idea behind a keypoint position regressor. Cylindrical coordinates $(\hat{\rho}, \hat{\theta}, \hat{z})$ of one of keypoint relative to the supervoxel center are regressed in each non-empty supervoxel. Dashed lines indicate voxel boundaries.
Abstract
The paper presents a deep neural network-based method for global and local descriptors extraction from a point cloud acquired by a rotating 3D LiDAR. The descriptors can be used for two-stage 6DoF relocalization. First, a course position is retrieved by finding candidates with the closest global descriptor in the database of geo-tagged point clouds. Then, the 6DoF pose between a query point cloud and a database point cloud is estimated by matching local descriptors and using a robust estimator such as RANSAC. Our method has a simple, fully convolutional architecture based on a sparse voxelized representation. It can efficiently extract a global descriptor and a set of keypoints with local descriptors from large point clouds with tens of thousand points. Our code and pretrained models are publicly available on the project website.
Jacek Komorowski
Assistant Professor
Monika Wysoczańska
PhD Student
