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Simultaneous Localization, Mapping and Moving Object Tracking

Chieh-Chih Wang, Chuck Thorpe, Martial Hebert, Sebastian Thrun and H. Durrant-Whyte
Journal Article, Carnegie Mellon University, The International Journal of Robotics Research, Vol. 26, No. 6, June, 2007

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Abstract

Simultaneous localization, mapping and moving object tracking (SLAMMOT) involves both simultaneous localization and mapping (SLAM) in dynamic en- vironments and detecting and tracking these dynamic objects. In this paper, we establish a mathematical framework to integrate SLAM and moving ob- ject tracking. We describe two solutions: SLAM with generalized objects, and SLAM with detection and tracking of moving objects (DATMO). SLAM with generalized objects calculates a joint posterior over all generalized objects and the robot. Such an approach is similar to existing SLAM algorithms, but with additional structure to allow for motion modeling of generalized objects. Un- fortunately, it is computationally demanding and generally infeasible. SLAM with DATMO decomposes the estimation problem into two separate estima- tors. By maintaining separate posteriors for stationary objects and moving objects, the resulting estimation problems are much lower dimensional then SLAM with generalized objects. Both SLAM and moving object tracking from a moving vehicle in crowded urban areas are daunting tasks. Based on the SLAM with DATMO framework, we propose practical algorithms which deal with issues of perception modeling, data association, and moving object

BibTeX Reference
@article{Wang-2007-9753,
title = {Simultaneous Localization, Mapping and Moving Object Tracking},
author = {Chieh-Chih Wang and Chuck Thorpe and Martial Hebert and Sebastian Thrun and H. Durrant-Whyte},
booktitle = {The International Journal of Robotics Research},
notes = {(to appear)},
school = {Robotics Institute , Carnegie Mellon University},
month = {June},
year = {2007},
volume = {26},
number = {6},
address = {Pittsburgh, PA},
}
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