Efficient, iterative, sensor based 3-D map building using rating functions in configuration space

As recent research is directed towards flexible and autonomous robot behaviour, a priori knowledge is increasingly replaced by data obtained automatically by external sensors of the robot. Much effort already has been made regarding the integration of different types of sensor data into reliable world representations. Yet, the aspect of guiding robot perception in a systematic manner often has been neglected. Closing this gap, we present a framework addressing the efficient exploration of an unknown three-dimensional environment. A natural approach to this problem is a repeated "planning-sensing-updating"-cycle. In this paper special attention is paid on planning: Based on the current, incomplete world model and on constraints respecting the robot's motions, rating functions in configuration space are defined. When planning sensing actions, configurations with high ratings are chosen. Thus, a fast and efficient exploration of the workspace is guaranteed. Our approach works with miscellaneous robot architectures, including mobile systems as well as manipulators with many degrees of freedom and redundancies. The inverse kinematic solution of the robot has not to be known. The sensors to be used have to provide range measurements, like laser scanners, stereo or sonar systems.