EgoNN: Egocentric Neural Network for Point Cloud Based 6DoF Relocalization at the City Scale

• URL: http://arxiv.org/abs/2110.12486v1
• Date: Sun, 24 Oct 2021 16:46:57 GMT
• Title: EgoNN: Egocentric Neural Network for Point Cloud Based 6DoF Relocalization at the City Scale
• Authors: Jacek Komorowski, Monika Wysoczanska and Tomasz Trzcinski
• Abstract summary: 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. Our method has a simple, fully convolutional architecture based on a sparse voxelized representation. Our code and pretrained models are publicly available on the project website.
• Score: 15.662820454886202
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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