Show All
Today Last Week Last Month
 Search for a specific document
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)

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.

  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 = {}