This Article Free Full Text (Free PDF) Free Multimedia References Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Similar articles in Web of Science Alert me to new issues of the journal Add to Saved Citations Download to citation manager Request Permissions Request Reprints Add to My Marked Citations Citing Articles Citing Articles via Web of Science (3) Citing Articles via Google Scholar Citing Articles via Scopus Google Scholar Articles by Wang, C.-C. Articles by Durrant-Whyte, H. Search for Related Content Social Bookmarking                         What's this?

Simultaneous Localization, Mapping and Moving Object Tracking

Department of Computer Science and Information Engineering and Graduate Institute of Networking and Multimedia National Taiwan University Taipei 106, Taiwan bobwang{at}ntu.edu.tw

Charles Thorpe

Qatar Campus Carnegie Mellon University Pittsburgh, PA 15289, USA cet{at}qatar.cmu.edu

Sebastian Thrun

The AI group Stanford University Stanford, CA 94305, USA thrun{at}stanford.edu

Martial Hebert

The Robotics Institute Carnegie Mellon University Pittsburgh, PA 15213, USA hebert{at}ri.cmu.edu

Hugh Durrant-Whyte

The ARC Centre of Excellence for Autonomous Systems The University of Sydney NSW 2006, Australia hugh{at}acfr.usyd.edu.au

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: 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. Unfortunately, it is computationally demanding and generally infeasible. SLAM with DATMO decomposes 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.

Key Words: mobile robotics • localization • mapping • tracking • detection • robotic perception

The International Journal of Robotics Research, Vol. 26, No. 9, 889-916 (2007)
DOI: 10.1177/0278364907081229


CiteULike    Complore    Connotea    Del.icio.us    Digg    Reddit    Technorati    Twitter    What's this?