SeqLPD: 大規模点群記述と系列マッチングを強化したループ閉じ込み検出
SeqLPD: Sequence Matching Enhanced Loop-Closure Detection Based on Large-Scale Point Cloud Description for Self-Driving Vehicles
自動運転車のためのループ閉じ込み検出を、深層学習による点群の大域的記述子と粗から細への系列マッチング戦略を組み合わせて高精度かつリアルタイムに解決する手法を提案した。
著者: Zhe Liu, Chuanzhe Suo, Shunbo Zhou, Huanshu Wei, Yingtian Liu, Hesheng Wang, Yun-Hui Liu
分類: cs.CV, cs.RO
原文アブストラクト
Place recognition and loop-closure detection are main challenges in the localization, mapping and navigation tasks of self-driving vehicles. In this paper, we solve the loop-closure detection problem by incorporating the deep-learning based point cloud description method and the coarse-to-fine sequence matching strategy. More specifically, we propose a deep neural network to extract a global descriptor from the original large-scale 3D point cloud, then based on which, a typical place analysis approach is presented to investigate the feature space distribution of the global descriptors and select several super keyframes. Finally, a coarse-to-fine strategy, which includes a super keyframe based coarse matching stage and a local sequence matching stage, is presented to ensure the loop-closure detection accuracy and real-time performance simultaneously. Thanks to the sequence matching operation, the proposed approach obtains an improvement against the existing deep-learning based methods. Experiment results on a self-driving vehicle validate the effectiveness of the proposed loop-closure detection algorithm.
