An unsupervised learning approach toward automatic selection of recognition parameters for mobile robot navigation in less structured environments

Autonomous mobile robots are expected to act in less structured environment such as outdoor environment, specifically aiming for agricultural use. In such environments, it is desired that the recognition of environments for detecting pathway for the robot can be prepared with less processing cost of an engineer's hand-tuning. This paper presents an unsupervised learning approach to support the acquisition of features required for automatic selection of sensor features and parameters for sensor information processing. As an example of sensor feature, lines are detected from a 2D image obtained by a depth camera, their corresponding 3D configurations are calculated using depth information, and the line segments in 3D space can be used as cues to detect pathways for a mobile robot. Resolution of the image is one parameter that requires adjustment depending on the environment. The proposed method can obtain the necessary features for automatic selection of appropriate resolution parameters by performing mean shift clustering of 3D line configuration for different resolution parameters. The proposed unsupervised learning approach has been tested in multiple environments with diverse appearances and suggested that it is an effective method for automatic selection of appropriate parameters depending on the environment.