スパースガウス過程による軽量点群表現
Light-Weight Pointcloud Representation with Sparse Gaussian Process
スパースガウス過程を用いて点群をコンパクトに符号化し、自由空間と占有空間を単一モデルで表現することで、通信量を70〜100倍削減する手法を提案した。
著者: Mahmoud Ali, Lantao Liu
分類: cs.RO
原文アブストラクト
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.
