Simulation Research on Simultaneous Robot Localization and Mapping Based on Particle Filter

Abstract

The ability to simultaneous localization and mapping is a predetermination of antomomous robots. Now, few approaches can manage the environment with masses of landmarks. The posterior distribution over robot pose and landmark locations was estimated with paticle filter and Kalman Filter respectively. The key idea of the algorithm is factorating of the Bayes filter into an estimation of robot path estimation and an estimation of landmarks. To avoid the depletion problem, the particle population was injected during the update phase with a small number of particles created directly from the sensor data as well as using the weights of the particles to decide which ones were going to be progated forward. Simulation results demonstrate 5000 landmarks with 100 particles can be handled successfully.