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Robust Visual SLAM Across Seasons
Tayyab Naseer, Michael Ruhnke, Cyrill Stachniss, Luciano Spinello, Wolfram Burgard
Proc. of the IEEE Int. Conf. on Intelligent Robots and Systems (IROS)
2015
naseer15iros.pdf
Notes:
In this paper, we present an appearance-based visual SLAM approach that focuses on detecting loop closures across seasons. Given two image sequences, our method first
extracts one descriptor per image for both sequences using
a deep convolutional neural network. Then, we compute a
similarity matrix by comparing each image of a query sequence with a database. Finally, based on the similarity matrix, we formulate a flow network problem and compute matching hypotheses between sequences. In this way, our approach can handle partially matching routes, loops in the trajectory and different speeds of the robot. With a matching hypothesis as loop closure information and the odometry information of the robot, we formulate a graph based SLAM problem and compute a joint maximum likelihood trajectory.
BibTeX:
@inproceedings{naseer15iros,
author = {Naseer, Tayyab and Ruhnke, Michael and Spinello, Luciano and Stachniss, Cyrill and Burgard, Wolfram},
title = {Robust Visual SLAM Across Seasons},
booktitle = {Proc.~of the IEEE Int.~Conf.~on Intelligent Robots and Systems (IROS)},
year = 2015,
url = {http://ais.informatik.uni-freiburg.de/publications/papers/naseer15iros.pdf}
}
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