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.
Highlights
Establishing the spatial and temporal relationships among a robot, stationary objects and moving objects in a scene serves as a basis for scene understanding
Such an approach is similar to existing simultaneous localization and mapping (SLAM) algorithms, but with additional structure to allow for motion modeling of generalized objects
Localization is the process of establishing the spatial relationships between the robot and stationary objects, mapping is the process of establishing the spatial relationships among stationary objects, and moving object tracking is the process of establishing the spatial and temporal relationships between moving objects and the robot or between moving and stationary objects
Summary
Establishing the spatial and temporal relationships among a robot, stationary objects and moving objects in a scene serves as a basis for scene understanding. In Wang et al (2003b), we established a mathematical framework to integrate SLAM and moving object tracking, which provides a solid basis for understanding and solving the whole problem, simultaneous localization, mapping and moving object tracking, or SLAMMOT. By maintaining separate posteriors for stationary objects and moving objects, the resulting estimation problems are much lower dimensional than SLAM with generalized objects This makes it feasible to update both filters in real time. There are significant practical issues to be considered in bridging the gap between the presented theory and its applications to real problems such as driving safely at high speeds in crowded urban areas These issues arise from a number of implicit assumptions in perception modeling and data association.
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