One of the tasks in operating a robot autonomously is to identify road conditions. For example, when the robot navigates outdoors, such as in a park, it is preferable to run on a flat, paved road while avoiding lawns and puddles. In this research, we propose a method to recognize the road conditions using the reflection intensity obtained from the laser range sensor. In the proposed method, the road surface on which the reflection intensity is measured is divided into polar grids, and the distribution of the reflection intensity in each grid is expressed using kernel density estimation. By comparing this distribution with reference data, it is shown that road surface conditions such as lawns, puddles, and paved roads can be identified. In addition, we report the results of experiments using this method in a real environment with existing methods, and report future issues.
