期刊名称：International Journal of Advanced Robotic Systems
印刷版ISSN：1729-8806
电子版ISSN：1729-8814
出版年度：2021
卷号：18
期号：5
DOI：10.1177/17298814211050560
语种：English
出版社：SAGE Publications
摘要：Humans maintain good memory and recognition capability of previous environments when they are learning about new ones. Thus humans are able to continually learn and increase their experience. It is also obvious importance for autonomous mobile robot. The simultaneous localization and mapping system plays an important role in localization and navigation of robot. The loop-closure detection method is an indispensable part of the relocation and map construction, which is critical to correct mappoint errors of simultaneous localization and mapping. Existing visual loop-closure detection methods based on deep learning are not capable of continual learning in terms of cross-scene environment, which bring a great limitation to the application scope. In this article, we propose a novel end-to-end loop-closure detection method based on continual learning, which can effectively suppress the decline of the memory capability of simultaneous localization and mapping system by introducing firstly the orthogonal projection operator into the loop-closure detection to overcome the catastrophic forgetting problem of mobile robot in large-scale and multi-scene environments. Based on the three scenes from public data sets, the experimental results show that the proposed method has a strong capability of continual learning in the cross-scene environment where existing state-of-the-art methods fail.
