Online Simultaneous Localization and Mapping with Detection and Tracking of Moving Objects: Theory and Results from a Ground Vehicle in Crowded Urban Areas

Chieh-Chih Wang, Chuck Thorpe, and Sebastian Thrun
IEEE International Conference on Robotics and Automation, May, 2003.


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Abstract
The simultaneous localization and mapping (SLAM) with detection and tracking of moving objects (DATMO) problem is not only to solve the SLAM problem in dynamic environments but also to detect and track these dynamic objects. In this paper, we derive the Bayesian formula of the SLAM with DATMO problem, which provides a solid basis for understanding and solving this problem. In addition, we provide a practical algorithm for performing DATMO from a moving platform equipped with range sensors. The probabilistic approach to solve the whole problem has been implemented with the Navlab11 vehicle. More than 100 miles of experiments in crowded urban areas indicated that SLAM with DATMO is indeed feasible.

Notes
Associated Center(s) / Consortia: Vision and Autonomous Systems Center
Associated Lab(s) / Group(s): NavLab
Associated Project(s): Transit Bus Collision Warning Systems and Simultaneous Localization and Mapping with Detection, Tracking, and Classification of Moving Objects

Text Reference
Chieh-Chih Wang, Chuck Thorpe, and Sebastian Thrun, "Online Simultaneous Localization and Mapping with Detection and Tracking of Moving Objects: Theory and Results from a Ground Vehicle in Crowded Urban Areas," IEEE International Conference on Robotics and Automation, May, 2003.

BibTeX Reference
@inproceedings{Wang_2003_4357,
   author = "Chieh-Chih Wang and Chuck Thorpe and Sebastian Thrun",
   title = "Online Simultaneous Localization and Mapping with Detection and Tracking of Moving Objects: Theory and Results from a Ground Vehicle in Crowded Urban Areas",
   booktitle = "IEEE International Conference on Robotics and Automation",
   month = "May",
   year = "2003",
}