Using visual features to build topological maps of indoor environments

This paper addresses the problem of localization and map construction by a mobile robot in an indoor environment. Instead of trying to build high-fidelity geometric maps, we focus on constructing topological maps, as they are lees sensitive to poor odometry estimates and position errors. We propose a method for incrementally building topological maps for a robot, which uses a panoramic camera to obtain images at various locations along its path and uses the features it tracks in the images to update the topological map. The method is very general and does not require the environment to have uniquely distinctive features.

[1]  Daniel P. Huttenlocher,et al.  Comparing Images Using the Hausdorff Distance , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Christiaan J. J. Paredis,et al.  Heterogeneous Teams of Modular Robots for Mapping and Exploration , 2000, Auton. Robots.

[3]  Karsten P. Ulland,et al.  Vii. References , 2022 .

[4]  Gaurav S. Sukhatme,et al.  Localization for mobile robot teams using maximum likelihood estimation , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[5]  José Santos-Victor,et al.  Omni-directional Visual Navigation , 1999 .

[6]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[7]  Wolfram Burgard,et al.  A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots , 1998, Auton. Robots.

[8]  Stephen R. Marsland,et al.  Learning globally consistent maps by relaxation , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[9]  Maria L. Gini,et al.  Performance of a distributed robotic system using shared communications channels , 2002, IEEE Trans. Robotics Autom..

[10]  S. Derrien,et al.  Approximating a single viewpoint in panoramic imaging devices , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[11]  Ben J. A. Kröse,et al.  A probabilistic model for appearance-based robot localization , 2001, Image Vis. Comput..

[12]  Illah R. Nourbakhsh,et al.  Appearance-based place recognition for topological localization , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[13]  Verena V. Hafner Cognitive Maps for Navigation in Open Environments , 2000 .

[14]  Dieter Fox,et al.  Markov localization - a probabilistic framework for mobile robot localization and navigation , 1998 .

[15]  Wolfram Burgard,et al.  A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots , 1998, Machine Learning.