Abstract
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.
Abstract (translated)
本论文提出了一种框架,用于高效通信和存储高保真的点云传感器观察。该方法利用稀疏高斯过程将点云编码成紧凑形式。我们的方法只使用一个模型(一个2D稀疏高斯过程)来代表自由空间和占用空间,而不必使用现有的两个模型框架(两个3D高斯混合模型)。我们通过提出一种基于方差采样技术的有效区分自由空间和占用空间的方法来实现这一点。新表示需要的内存 footprint更少,可以跨越有限的带宽通信通道传输。该框架在模拟中进行广泛评估,同时也使用具有3D LiDAR的真实的移动机器人进行演示。我们的方法比发送原始点云的通信速率下降了70到100倍。
URL
https://arxiv.org/abs/2301.11251
