A speeded-up online incremental vision-based loop-closure detection for long-term SLAM

• Main Authors: Aram Kawewong, Noppharit Tongprasit, Osamu Hasegawa
• Format: Journal
• Published: 2018
• Online Access: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84885606487&origin=inward; http://cmuir.cmu.ac.th/jspui/handle/6653943832/47415
• Institution: Chiang Mai University

An online incremental method of vision-only loop-closure detection for long-term robot navigation is proposed. The method is based on the scheme of direct feature matching which has recently become more efficient than the Bag-of-Words scheme in many challenging environments. The contributions of the paper are the application of hierarchical k-means to speed-up feature matching time and the improvement of the score calculation technique used to determine the loop-closing location. As a result, the presented method runs quickly in long term while retaining high accuracy. The searching cost has been markedly reduced. The proposed method requires no any off-line dictionary generation processes. It can start anywhere at any times. The evaluation has been done on standard well-known datasets being challenging in perceptual aliasing and dynamic changes. The results show that the proposed method offers high precision-recall in large-scale different environments with real-time computation comparing to other vision-only loop-closure detection methods. © 2013 Taylor & Francis and The Robotics Society of Japan.