This paper presents a framework to represent high-fidelity pointcloud sensor observations for efficient communication 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 limitedbandwidth communication channels. The framework is extensively evaluated in simulation and it is also demonstrated using a real mobile robot equipped with a 3D LiDAR. Our method results in a 70 to 100 times reduction in the communication rate compared to sending the raw pointcloud.

翻译：本文提出一种框架，用于表示高保真点云传感器观测数据，以实现高效通信与存储。该方法利用稀疏高斯过程将点云编码为紧凑形式。与现有采用两个模型（两个3D高斯混合模型）的框架不同，本文仅使用单一模型（一个2D稀疏高斯过程）同时表示自由空间与占据空间。为此，我们提出一种基于方差的采样技术，有效区分自由空间与占据空间。新表示形式所需内存足迹更小，且可通过有限带宽通信信道传输。该框架在仿真环境中得到广泛评估，并在配备3D激光雷达的真实移动机器人上进行了验证。与传输原始点云相比，本方法将通信速率降低70至100倍。