Fusion of aerial images and sensor data from a ground vehicle for improved semantic mapping

This work investigates the use of semantic information to link ground level occupancy maps and aerial images. A ground level semantic map, which shows open ground and indicates the probability of cells being occupied by walls of buildings, is obtained by a mobile robot equipped with an omnidirectional camera, GPS and a laser range finder. This semantic information is used for local and global segmentation of an aerial image. The result is a map where the semantic information has been extended beyond the range of the robot sensors and predicts where the mobile robot can find buildings and potentially driveable ground. Key words: semantic mapping, semi-supervised learning, aerial image, mobile robot

• Type: Journal
• Year: 2008
• Where: RAS
• Authors: Martin Persson, Tom Duckett, Achim J. Lilienthal
• Keywords: Aerial Image, Mobile Robot, RAS 2008, Semantic Information