SeqSLAM : visual route-based navigation for sunny summer days and stormy winter nights
Milford, Michael & Wyeth, Gordon (2012) SeqSLAM : visual route-based navigation for sunny summer days and stormy winter nights. In Papanikolopoulos, Nikos (Ed.) Proceedings of the 2012 IEEE International Conferece on Robotics and Automation (ICRA), IEEE, River Centre, Saint Paul, Minnesota, pp. 1643-1649.
Learning and then recognizing a route, whether travelled during the day or at night, in clear or inclement weather, and in summer or winter is a challenging task for state of the art algorithms in computer vision and robotics. In this paper, we present a new approach to visual navigation under changing conditions dubbed SeqSLAM. Instead of calculating the single location most likely given a current image, our approach calculates the best candidate matching location within every local navigation sequence. Localization is then achieved by recognizing coherent sequences of these “local best matches”. This approach removes the need for global matching performance by the vision front-end - instead it must only pick the best match within any short sequence of images. The approach is applicable over environment changes that render traditional feature-based techniques ineffective. Using two car-mounted camera datasets we demonstrate the effectiveness of the algorithm and compare it to one of the most successful feature-based SLAM algorithms, FAB-MAP. The perceptual change in the datasets is extreme; repeated traverses through environments during the day and then in the middle of the night, at times separated by months or years and in opposite seasons, and in clear weather and extremely heavy rain. While the feature-based method fails, the sequence-based algorithm is able to match trajectory segments at 100% precision with recall rates of up to 60%.
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|Item Type:||Conference Paper|
|Keywords:||Cameras, Navigation, Robot sensing systems, Trajectory, Vectors, Videos, Visualization|
|Subjects:||Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Control Systems Robotics and Automation (090602)|
|Divisions:||Current > Schools > School of Earth, Environmental & Biological Sciences
Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2012 IEEE|
|Copyright Statement:||This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible|
|Deposited On:||10 Jul 2012 23:34|
|Last Modified:||12 Sep 2016 01:26|
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