CNN特徴抽出を用いた点群による場所認識
Point-cloud-based place recognition using CNN feature extraction
カラー画像で事前学習したCNNで距離画像から特徴を抽出し、点群ベースの場所認識を高精度化した研究。照明・回転不変で移動物体に頑健なシステムと新データセットを提案。
著者: Ting Sun, Ming Liu, Haoyang Ye, Dit-Yan Yeung
分類: cs.CV, cs.RO
原文アブストラクト
This paper proposes a novel point-cloud-based place recognition system that adopts a deep learning approach for feature extraction. By using a convolutional neural network pre-trained on color images to extract features from a range image without fine-tuning on extra range images, significant improvement has been observed when compared to using hand-crafted features. The resulting system is illumination invariant, rotation invariant and robust against moving objects that are unrelated to the place identity. Apart from the system itself, we also bring to the community a new place recognition dataset containing both point cloud and grayscale images covering a full $360^\circ$ environmental view. In addition, the dataset is organized in such a way that it facilitates experimental validation with respect to rotation invariance or robustness against unrelated moving objects separately.
