Accuracy Improvement of Semantic Segmentation Using Appropriate Datasets for Robot Navigation

Abstract

The use of detailed metric maps for autonomous movement of robots has been popularized in recent times. Three-dimensional sensing devices, such as 3D LiDAR and RADAR, which are expensive yet indispensable, are utilized to generate these metric maps, and ultimately, perform localization. To reduce the cost of sensing devices, we try to realize autonomous movement of a robot using only cheap image sensors, such as webcams. For robot navigation, image processing tends to be applied to collision avoidance by finding obstacles. In contrast, our approach does not use visual object detection but adopts path planning based on movable area extraction from input images using semantic segmentation. To obtain accurate results for visual navigation, this paper proposes the use of novel datasets for semantic segmentation. Experimental results showed that ICNet could extract the movable area with more than 99% accuracy if it was trained with appropriate datasets, and a robot can run automatically based on the extracted movable area.