Particle attraction localisation

In this paper, we present an original method for Bayesian localisation based on particle approximation. Our method overcomes a majority of problems inherent in previous Kalman filter and Bayesian approaches, including the recent Monte Carlo localisation methods. The algorithm converges quickly to any desired precision. It does not over-converge in the case of highly accurate sensor data and thus does not require a mixture-based approach. Also, the algorithm recovers well from random repositioning. These benefits are not hindered by computation which can be performed in real time on low powered processors. Further, the algorithm is intuitive and easy to implement. This algorithm is evaluated in simulation and has been applied to our entrant in the Sony four legged league of RoboCup, where it has been tested over many hours of international competition.

Authors

• Damien George
• Nick Barnes

Keywords

• State-space methods
• Bayesian methods
• Orbital robotics
• Monte Carlo methods
• Testing
• Robot sensing systems
• Probability distribution
• Particle filters
• Computer science
• Software engineering
• Kalman Filter
• Posterior Probability
• Gaussian Kernel
• State Space
• Average Error
• Dynamic Information
• Particle Filter
• Convergence Time
• Normalization Constant
• Sensor Noise
• Sensor Readings
• Ambiguous Situations
• Odometry
• Transformation Kinetics
• Static Information
• Landmark Detection
• Robot Pose
• Robot Localization
• Positive X-axis
• Bayesian Filtering
• Regions Of The State Space
• Pair Of Particles