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 realtime 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 selfcollected 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.

LUNet: An Efficient Network for 3D LiDAR Point Cloud Semantic Segmentation Based on EndtoEndLearned 3D Features and UNet We propose LUNetfor LiDAR UNet, a new method for the semantic segmentation of a 3D LiDAR point cloud. Instead of applying some global 3D segmentation method such as PointNet, we propose an endtoend architecture for LiDAR point cloud semantic segmentation that efficiently solves the problem as an image processing problem. We first extract highlevel 3D features for each point given its 3D neighbors. Then, these features are projected into a 2D multichannel rangeimage by considering the topology of the sensor. Thanks to these learned features and this projection, we can finally perform the segmentation using a simple UNet segmentation network, which performs very well while being very efficient. In this way, we can exploit both the 3D nature of the data and the specificity of the LiDAR sensor. This approach outperforms the stateoftheart by a large margin on the KITTI dataset, as our experiments show. Moreover, this approach operates at 24fps on a single GPU. This is above the acquisition rate of common LiDAR sensors which makes it suitable for realtime applications.