動き属性を考慮した局所特徴記述子の学習による視覚ベース自己位置推定
Learning Local Feature Descriptor with Motion Attribute for Vision-based Localization
動く物体の特徴点を除外するための動き属性推定と特徴記述を1つのFCNで同時に行うMD-Netを提案し、視覚的自己位置推定の精度を向上させた。
著者: Yafei Song, Di Zhu, Jia Li, Yonghong Tian, Mingyang Li
分類: cs.CV, cs.RO
原文アブストラクト
In recent years, camera-based localization has been widely used for robotic applications, and most proposed algorithms rely on local features extracted from recorded images. For better performance, the features used for open-loop localization are required to be short-term globally static, and the ones used for re-localization or loop closure detection need to be long-term static. Therefore, the motion attribute of a local feature point could be exploited to improve localization performance, e.g., the feature points extracted from moving persons or vehicles can be excluded from these systems due to their unsteadiness. In this paper, we design a fully convolutional network (FCN), named MD-Net, to perform motion attribute estimation and feature description simultaneously. MD-Net has a shared backbone network to extract features from the input image and two network branches to complete each sub-task. With MD-Net, we can obtain the motion attribute while avoiding increasing much more computation. Experimental results demonstrate that the proposed method can learn distinct local feature descriptor along with motion attribute only using an FCN, by outperforming competing methods by a wide margin. We also show that the proposed algorithm can be integrated into a vision-based localization algorithm to improve estimation accuracy significantly.
