Simultaneous Localization, Mapping and Moving Object Tracking

  • Authors:

  • Chieh-Chih Wang;Charles Thorpe;Sebastian Thrun;Martial Hebert;Hugh Durrant-Whyte

  • Affiliations:

  • Department of Computer Science and Information Engineeringand Graduate Institute of Networking and Multimedia National Taiwan UniversityTaipei 106, Taiwan;Qatar Campus Carnegie Mellon University Pittsburgh,PA 15289, USA;The AI group Stanford University Stanford, CA 94305,USA;The Robotics Institute Carnegie Mellon University Pittsburgh,PA 15213, USA;The ARC Centre of Excellence for Autonomous SystemsThe University of Sydney NSW 2006, Australia

  • Venue:
  • International Journal of Robotics Research
  • Year:
  • 2007

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Abstract

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.