Journal of the Brazilian Society of Mechanical Sciences and Engineering
Print version ISSN 1678-5878
On-line version ISSN 1806-3691
BECKER, Marcelo et al. 2D laser-based probabilistic motion tracking in urban-like environments. J. Braz. Soc. Mech. Sci. & Eng. [online]. 2009, vol.31, n.2, pp.83-96. ISSN 1678-5878. http://dx.doi.org/10.1590/S1678-58782009000200001.
All over the world traffic injuries and fatality rates are increasing every year. The combination of negligent and imprudent drivers, adverse road and weather conditions produces tragic results with dramatic loss of life. In this scenario, the use of mobile robotics technology onboard vehicles could reduce casualties. Obstacle motion tracking is an essential ability for car-like mobile robots. However, this task is not trivial in urban environments where a great quantity and variety of obstacles may induce the vehicle to take erroneous decisions. Unfortunately, obstacles close to its sensors frequently cause blind zones behind them where other obstacles could be hidden. In this situation, the robot may lose vital information about these obstructed obstacles that can provoke collisions. In order to overcome this problem, an obstacle motion tracking module based only on 2D laser scan data was developed. Its main parts consist of obstacle detection, obstacle classification, and obstacle tracking algorithms. A motion detection module using scan matching was developed aiming to improve the data quality for navigation purposes; a probabilistic grid representation of the environment was also implemented. The research was initially conducted using a MatLab simulator that reproduces a simple 2D urban-like environment. Then the algorithms were validated using data samplings in real urban environments. On average, the results proved the usefulness of considering obstacle paths and velocities while navigating at reasonable computational costs. This, undoubtedly, will allow future controllers to obtain a better performance in highly dynamic environments.
Keywords : motion tracking; obstacle classification; Kalman Filter; urban-like environment.