Particle Filter Localization on Continuous Occupancy Maps

Abstract

Occupancy grid maps have been widely used for robot localization. Despite the popularity, this representation has some limitations, such as requirement of discretization of the environment, assumption of independence between grid cells and necessity of dense sensor data. Suppressing these limitations can improve the localization performance, but requires a different representation of the environment. Gaussian process occupancy map (GPOM) is a novel representation based on Gaussian Process that enables the construction of continuous maps (i.e. without discretization) using few laser measurements. This paper addresses a new localization method that uses GPOM to estimate the robot pose in areas not directly observed during mapping and generally provides higher accuracy compared to occupancy grid maps localization. Specifically, we devised a novel likelihood model based on the multivariate normal probability density function and adapted the particle filter localization method to work with GPOM. Experiments showed localization errors more than three times lower in comparison with particle filter localization using occupancy grid maps.