Sensor models and multisensor integration
International Journal of Robotics Research - Special Issue on Sensor Data Fusion
Quadruped Robot Navigation Considering the Observational Cost
RoboCup 2001: Robot Soccer World Cup V
Exploring artificial intelligence in the new millennium
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Tactic-based motion modeling and multi-sensor tracking
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Map-Based multiple model tracking of a moving object
RoboCup 2004
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Multi-observation sensor resetting localization with ambiguous landmarks
Autonomous Robots
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To act intelligently in dynamic environments, mobile robots must estimate object positions using information obtained from a variety of sources. We formally describe the problem of estimating the state of objects where a robot can only task its sensors to view one object at a time. We contribute an object tracking method that generates and maintains multiple hypotheses consisting of probabilistic state estimates that are generated by the individual information sources. These different hypotheses can be generated by the robot's own prediction model and by communicating robot team members. The multiple hypotheses are often spatially disjoint and cannot simultaneously be verified by the robot's limited sensors. Instead, the robot must decide towards which hypothesis its sensors should be tasked by evaluating each hypothesis on its likelihood of containing the object. Our contributed algorithm prioritizes the different hypotheses, according to rankings set by the expected uncertainty in the object's motion model, as well as the uncertainties in the sources of information used to track their positions. We describe the algorithm in detail and show extensive empirical results in simulation as well as experiments on actual robots that demonstrate the effectiveness of our approach.