Henrik Andreasson, André Treptow and Tom Duckett
Localization for Mobile Robots using Panoramic Vision, Local Features and Particle Filter
Proc. of IEEE Int. Conf. on Robotics and Automation (ICRA), 2005, pp. 3348-3353
Abstract: In this paper we present a vision-based approach to self-localization that uses a novel scheme to integrate feature-based matching of panoramic images with Monte Carlo localization. A specially modified version of Lowe's SIFT algorithm is used to match features extracted from local interest points in the image, rather than using global features calculated from the whole image. Experiments conducted in a large, populated indoor environment (up to 5 persons visible) over a period of several months demonstrate the robustness of the approach, including kidnapping and occlusion of up to 90% of the robot's field of view.
Paper: [PDF (0.3MB)]
  AUTHOR = {Andreasson, Henrik and Treptow, Andr\'{e} and Duckett, Tom},
  TITLE = {Self-Localization in Non-Stationary Environments using Omni-Directional Vision},
  BOOKTITLE = {Proc. IEEE Int. Conf. on Robotics and Automation},
  YEAR = {2005},
  PAGES = {3348--3353}