Host: The Japan Society of Mechanical Engineers
Name : [in Japanese]
Date : June 06, 2021 - June 08, 2021
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 the environment 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.
