Optimal view path planning for visual SLAM
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
A novel loop closure detection method in monocular SLAM
Intelligent Service Robotics
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This paper describes BoWSLAM, a scheme for a robot to reliably navigate and map previously unknown environments, in real time, using monocular vision alone. BoWSLAM can navigate challenging dynamic and self-similar environments and can recover from gross errors. Key innovations allowing this include new uses for the bag-of-words image representation; this is used to select the best set of frames from which to reconstruct positions and to give efficient wide-baseline correspondences between many pairs of frames, providing multiple position hypotheses. A graph-based representation of these position hypotheses enables the modeling and optimization of errors in scale in a dual graph and the selection of only reliable position estimates in the presence of gross outliers. BoWSLAM is demonstrated mapping a 25-min, 2.5-km trajectory through a challenging and dynamic outdoor environment without any other sensor input, considerably farther than previous single-camera simultaneous localization and mapping (SLAM) schemes. © 2010 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc.