Fast Detection of Arbitrary Planar Surfaces from Unreliable 3D Data

Man-made real-world environments are dominated by planar surfaces many of which constitute behavior relevant entities. Thus, the ability to perceive planar surfaces is vital for any embodied system operating in such environments, be it human or robotic. In this paper, we present an architecture for. detection and estimation of planar surfaces in the scene from calibrated stereo images. They are represented in a behavior-oriented way, focusing on geometrical properties that are relevant for enabling basic interaction between a robot and the planar surfaces it perceives. Ego-motion of the robot is compensated for by transforming the representations into a global coordinate system using the kinematics of the robot. Our architecture is able to detect and estimate arbitrary planar surfaces, regardless of their visual appearance, their geometrical properties other than planarity and their being static or arbitrarily moving. The latter is achieved by processing cach frame independently of the others. Stable representations are obtained by establishing spatio-temporal coherence between the single-frame representations of subsequent frames. Based on a RANSAC approach to plane fitting, our method is robust to unreliable 3D data such as obtained by local stereo correlation, for example. In our experiments using the Honda humanoid robot ASIMO, we show that our method is able to provide a robot in real-time with representations of planar surfaces in its environment that are sufficiently accurate for basic interaction.