Simultaneous Localization, Mapping and Moving Object Tracking

Author:

Wang Chieh-Chih1,Thorpe Charles2,Thrun Sebastian3,Hebert Martial4,Durrant-Whyte Hugh5

Affiliation:

1. Department of Computer Science and Information Engineering and Graduate Institute of Networking and Multimedia National Taiwan University Taipei 106, Taiwan

2. Qatar Campus Carnegie Mellon University Pittsburgh, PA 15289, USA

3. The AI group Stanford University Stanford, CA 94305, USA

4. The Robotics Institute Carnegie Mellon University Pittsburgh, PA 15213, USA

5. The ARC Centre of Excellence for Autonomous Systems The University of Sydney NSW 2006, Australia

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.

Publisher

SAGE Publications

Subject

Applied Mathematics,Artificial Intelligence,Electrical and Electronic Engineering,Mechanical Engineering,Modeling and Simulation,Software

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2. Tangible Surface-Based Interactions;Encyclopedia of Computer Graphics and Games;2024

3. Detecting Moving Objects Using a Novel Optical-Flow-Based Range-Independent Invariant;2023 IEEE 20th International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT (HONET);2023-12-04

4. Risk-Aware Trajectory Sampling for Quadrotor Obstacle Avoidance in Dynamic Environments;IEEE Transactions on Industrial Electronics;2023-12

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