SWARM
Multi-robot Multiple Hypothesis Tracking for pedestrian tracking with detection uncertainty
Nicolas A. Tsokas, Kostas J. Kyriakopoulos
- 发表年份
- 2011
- 引用次数
- 8
摘要
The problem of tracking walking people with a team of moving robots is tackled in this paper. We extend the Multiple Hypothesis Tracking method so as to handle measurements coming from multiple sensors and to allow for one-to-many associations between targets and measurements. Derivation of hypotheses probabilities accounts for the overlapping fields of view of the robots sensors and for uncertainty in detection. In the context of two experiments involving people walking among moving robots, the successful integration of our tracking algorithm to a real-world scenario is assessed.
关键词
Tracking (education)RobotArtificial intelligenceComputer scienceContext (archaeology)Computer visionPedestrianTracking systemMobile robotKalman filter
相关论文
OTHER
📊 26,957 引用
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
PERCEPTION
📊 22,245 引用
Artificial intelligence: a modern approach
1995
OTHER
📊 18,993 引用
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991
SWARM
📊 14,853 引用
A new optimizer using particle swarm theory
R.C. Eberhart, James Kennedy
2002