Selecting good corners for structure and motion recovery using a time-of-flight camera

In the robotics and computer vision communities, localization and mapping of an unknown environment is a well studied problem. To tackle this problem in real-time using a single camera, state-of-the-art Simultaneous Localization and Mapping (SLAM) or Structure from Motion (SfM) algorithms can be used. To create the model of the unknown environment, the camera moves and adds to the map from point to point, and assumes that these detected points are unique 3D corners. However, the scene usually contains false 3D corners, lying at e.g. occlusion boundaries. Inserting these points into the map may lead to SLAM failure or to less accurate estimations in SfM. In this work, a corner selection scheme is proposed that exploits the amplitude and depth signals of a Time-of- Flight (ToF) camera. The selection scheme detects false 3D corners based on a 3D cornerness measure. We then prove that the rejection of these corners increases the accuracy with a simulated SfM example and show the results of using our selection scheme with the ToF camera sequences.