Indoor mobile robot self-location based on particle filter

Focusing on issues of map-distortion caused by accumulated error in mapping with only odometer and sonar sensors, new infrared location lags as absolute landmarks are introduced and build a topological map. Then a grid map is also built in the topological map and landmark information updates related incremental map on Bayesian theory. As result, the hybrid map reduces the accumulated error of sensors and improves the stability of the built map. Moreover, improved particle filter is used for robot localization, which based on the high weight adaptive algorithm can solve particle degradation of the traditional particle filters, because high weight particles and the surrounding space describe robot pose with high probability. Finally, results of experiments show that the map is effectively build based on grid map guided by absolute landmarks in 250 cm × 500 cm area, and improved particle filter can finish self-localization, error of which is less than 2 cm. Besides, localization test proves that improved particle is effective to solve degradation of particles.