畳み込みオートエンコーダによる環境変化に頑健でコンパクトな視覚的位置記述
Condition-Invariant and Compact Visual Place Description by Convolutional Autoencoder
事前学習CNNの高次特徴を畳み込みオートエンコーダで低次元空間に写像し、照明変化に頑健で次元の小さい視覚的位置記述子を実現した研究。
著者: Hanjing Ye, Weinan Chen, Jingwen Yu, Li He, Yisheng Guan, Hong Zhang
分類: cs.CV, cs.RO
原文アブストラクト
Visual place recognition (VPR) in condition-varying environments is still an open problem. Popular solutions are CNN-based image descriptors, which have been shown to outperform traditional image descriptors based on hand-crafted visual features. However, there are two drawbacks of current CNN-based descriptors: a) their high dimension and b) lack of generalization, leading to low efficiency and poor performance in applications. In this paper, we propose to use a convolutional autoencoder (CAE) to tackle this problem. We employ a high-level layer of a pre-trained CNN to generate features, and train a CAE to map the features to a low-dimensional space to improve the condition invariance property of the descriptor and reduce its dimension at the same time. We verify our method in three challenging datasets involving significant illumination changes, and our method is shown to be superior to the state-of-the-art. For the benefit of the community, we make public the source code.
