A Solution for Bayesian Visual Loop Closure Detection Based on Local Invariant Features
Technical Report A-01-2013, Dep. Mathematics and Computer Science (UIB)
Visual loop closure detection in robotics is defi ned as the ability of recognizing previously seen places given the current image captured by the robot. The Bag-of-Words image representation has been widely used for these kinds of tasks. However, in this paper, an appearance-based approach for loop closure detection using local invariant features is proposed. Images are described using SIFT features and, for avoiding image-to-image comparisons, a set of randomized KD-trees are employed for feature matching. Further, a discrete Bayes filter is used for predicting loop closure candidates, whose likelihood is based on these KD-trees. The approach has been validated using monocular image sequences from different environments.