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Particle filters have recently been applied with great success to mobile robot localization. This success is mostly due to their simplicity and their ability to represent arbitrary, multi-modal densities over a robot's state space. The increased representational power, however, comes at the cost of higher computational complexity. In this paper we introduce adaptive real-time particle filters that greatly increase the performance of particle filters under limited computational resources. Ourdoi:10.1109/robot.2003.1242022 dblp:conf/icra/KwokFM03 fatcat:7zsdxkw6bzehrewjg743yutiau