Using EM to detect motion with mobile robots

In this paper we present a new method to detect motion from mobile platforms using laser range data. Motion can be found as differences in successive scans. The main challenge in doing so from a mobile platform is to distinguish differences originating from the platforms own motion from those caused by objects moving in the robot's vicinity. We tackle this combining localization and data association based on an a-priori obtained map. Localization and data association is done using the EM-algorithm. Elements, which are not in the map, are singled out as outliers. Subtracting them over time provides motion information. To reduce the complexity of each iteration step we chose a feature-based environment model, which reduces the computation required to a fraction. We use simulations to test our method against a known ground-truth. Results based on real-world data from the exhibition Robotics@Expo.02 are used to evaluate the proposed method under real-world conditions in highly dynamic situations with several hundred visitors per hour.

[1]  Wolfram Burgard,et al.  Experiences with an Interactive Museum Tour-Guide Robot , 1999, Artif. Intell..

[2]  Illah R. Nourbakhsh,et al.  The History of the Mobot Museum Robot Series: An Evolutionary Study , 2001, FLAIRS.

[3]  Roland Siegwart,et al.  Feature extraction and scene interpretation for map-based navigation and map building , 1998, Other Conferences.

[4]  Roland Siegwart,et al.  Visitor Flow Management using Human-Robot Interaction at Expo.02 , 2002 .

[5]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[6]  Wolfram Burgard,et al.  Using EM to Learn 3D Models of Indoor Environments with Mobile Robots , 2001, ICML.

[7]  Roland Siegwart,et al.  The interactive autonomous mobile system RoboX , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[8]  Roland Siegwart,et al.  Smooth and efficient obstacle avoidance for a tour guide robot , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[9]  Wolfram Burgard,et al.  Using EM to learn motion behaviors of persons with mobile robots , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[10]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[11]  Erwin Prassler,et al.  Fast and robust tracking of multiple moving objects with a laser range finder , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[12]  Jeff A. Bilmes,et al.  A gentle tutorial of the em algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models , 1998 .

[13]  Wolfram Burgard,et al.  Tracking multiple moving targets with a mobile robot using particle filters and statistical data association , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).