大規模場所認識のための深層スキャンコンテキスト記述子
DSC: Deep Scan Context Descriptor for Large-Scale Place Recognition
LiDAR点群を自己中心的にセグメント化し、グラフニューラルネットでセグメント間関係を集約する新たなグローバル記述子DSCを提案し、KITTIで既存手法を上回る場所認識性能を示した。
著者: Jiafeng Cui, Tengfei Huang, Yingfeng Cai, Junqiao Zhao, Lu Xiong, Zhuoping Yu
分類: cs.CV, cs.RO
原文アブストラクト
LiDAR-based place recognition is an essential and challenging task both in loop closure detection and global relocalization. We propose Deep Scan Context (DSC), a general and discriminative global descriptor that captures the relationship among segments of a point cloud. Unlike previous methods that utilize either semantics or a sequence of adjacent point clouds for better place recognition, we only use raw point clouds to get competitive results. Concretely, we first segment the point cloud egocentrically to acquire centroids and eigenvalues of the segments. Then, we introduce a graph neural network to aggregate these features into an embedding representation. Extensive experiments conducted on the KITTI dataset show that DSC is robust to scene variants and outperforms existing methods.
