Intersection detection based on recognition of drivable region using reflection light intensity

Abstract

In the conventional intersection recognition method, shape information is used. In order to recognize an intersection composed of roads divided by grass and asphalt, it is necessary to distinguish them. There is almost no difference in shape between grass and asphalt. For this reason, it is difficult to distinguish them by the method using shape information. Therefore, we use reflection intensity by LiDAR to distinguish grass and asphalt. In this paper, we compare the reflection intensity of the ground surface at points where distances from LiDAR are equal. The appropriate threshold for distinguishing materials of the ground is dynamically calculated for each distance using Discriminant Analysis Method. We accumulate the drivable region using the dynamically calculated threshold value in chronological order taking the relative movement amount into account. We add probabilistic processing to the drivable region accumulated in chronological order to extract more stable drivable region. Finally, an intersection is detected by combining the drivable region extracted using the reflection intensity and the drivable region extracted using the shape information. Intersection detection method uses Toe-Finding Algorithm. In order to show the usefulness of our method, we compare our method with two method, Partition Line Method and the proposed method unaccumulated the drivable region.

Keywords

• autonomous mobile robot
• intersection detection
• reflection light intensity
• static obstacle
• 3d-lidar