Light-Weight Pointcloud Representation with Sparse Gaussian Process

This paper presents a framework to represent high-fidelity pointcloud sensor observations for efficient com- munication and storage. The proposed approach exploits Sparse Gaussian Process to encode pointcloud into a compact form. Our approach represents both the free space and the occupied space using only one model (one 2D Sparse Gaussian Process) instead of the existing two-model framework (two 3D Gaussian Mixture Models). We achieve this by proposing a variance- based sampling technique that effectively discriminates between the free and occupied space. The new representation requires less memory footprint and can be transmitted across limited- bandwidth communication channels. The framework is exten- sively evaluated in simulation and it is also demonstrated using a real mobile robot equipped with a 3D LiDAR. Our method results in a 70∼100 times reduction in the communication rate compared to sending the raw pointcloud. We have provided a demonstration video1 and open-sourced our code 2.