Why-So-Deep: 既学習モデルを活用した視覚的位置認識の強化手法
Why-So-Deep: Towards Boosting Previously Trained Models for Visual Place Recognition
既存の学習済みモデルをそのまま使いつつ、空間情報を活用して画像検索の再現率を高めるMAQBOOLを提案し、低次元記述子で高性能を実現した。
著者: M. Usman Maqbool Bhutta, Yuxiang Sun, Darwin Lau, Ming Liu
分類: cs.CV, cs.RO
原文アブストラクト
Deep learning-based image retrieval techniques for the loop closure detection demonstrate satisfactory performance. However, it is still challenging to achieve high-level performance based on previously trained models in different geographical regions. This paper addresses the problem of their deployment with simultaneous localization and mapping (SLAM) systems in the new environment. The general baseline approach uses additional information, such as GPS, sequential keyframes tracking, and re-training the whole environment to enhance the recall rate. We propose a novel approach for improving image retrieval based on previously trained models. We present an intelligent method, MAQBOOL, to amplify the power of pre-trained models for better image recall and its application to real-time multiagent SLAM systems. We achieve comparable image retrieval results at a low descriptor dimension (512-D), compared to the high descriptor dimension (4096-D) of state-of-the-art methods. We use spatial information to improve the recall rate in image retrieval on pre-trained models.
