Localization of Mobile Robot by Particle Filter Considering Shape Information of Environment and Location of Water Puddles

In this paper, we propose a method to estimate robot pose accurately by considering environmental features for the particle filter. Particle filters have been widely used for pose estimation of mobile robots. However, the accuracy of the self-positioning estimation is degraded in environments where the shape features are scarce. Therefore, a novel likelihood function is designed by taking into account the environmental features for the particle filter. We improve the accuracy of the pose estimation by fusing the likelihood of the shape information and the likelihood of the binary attribute information in the water puddle for the particle filter.