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3D LiDAR Map Compression Using Deep Neural Network

3D LiDAR based maps provide basic foundation for localization and navigation of mobile robots. However, in outdoor scenarios, too many map points bring a huge computational burden to the processor, resulting in both slow calculation speed and poor real-time performance. In this paper, we propose a deep learning method to reduce the point cloud maps by applying and modifying the structure of PointNet. The recent PointNet architecture proposes a new approach to process 3D point clouds in map, which has made great performance in many tasks. In addition, we conduct experiments on self-collected dataset and evaluate the localization performance of compressing maps. The final results show that our model can implement map compression while keeping the localization error within an acceptable range.