| Volume 26 Issue 9 - Publication Date: 1 September 2007 |
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| Simultaneous
Localization, Mapping
and Moving Object
Tracking |
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| C.-C. Wang, Department of Computer Science and Information Engineering and
Graduate Institute of Networking and Multimedia
National Taiwan University
Taipei 106, Taiwan C. Thorpe, Qatar Campus
Carnegie Mellon University
Pittsburgh, PA 15289, USA S. Thrun, The AI group
Stanford University
Stanford, CA 94305, USA M. Hebert, The Robotics Institute
Carnegie Mellon University
Pittsburgh, PA 15213, USAand H. Durrant-Whyte The ARC Centre of Excellence for Autonomous Systems
The University of Sydney
NSW 2006, Australia |
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| Simultaneous localization, mapping and moving object tracking
(SLAMMOT) involves both simultaneous localization and mapping
(SLAM) in dynamic environments and detecting and tracking these
dynamic objects. In this paper, a mathematical framework is established
to integrate SLAM and moving object tracking. Two solutions
are described: SLAMwith 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. Unfortunately, it is computationally demanding
and generally infeasible. SLAM with DATMOdecomposes the estimation
problem into two separate estimators. By maintaining separate
posteriors for stationary objects and moving objects, the resulting estimation
problems are much lower dimensional than 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, practical algorithms are proposed
which deal with issues of perception modeling, data association, and
moving object detection. The implementation of SLAM with DATMO
was demonstrated using data collected from the CMU Navlab11 vehicle
at high speeds in crowded urban environments. Ample experimental
results shows the feasibility of the proposed theory and algorithms. |
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| Multimedia Key |
= Video |
 = Data |
= Code |
= Image |
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Extension |
Type |
Description |
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Example
1: video that illustrates the processing of the SLAM with DATMO approach. (390 MB) avi |
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Example
2: video that shows the processing of loop closing. (159 MB) mpg |
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Example
3: video illustratrating the processing
of multiple vehicle tracking. (4.6 MB) avi |
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Example
4: video illustrating multiple pedestrian
tracking. (2.5 MB) avi |
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Example
5: video
shows the 3D city modeling results. (19.6 MB) avi |
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