Segment-based structure from an imprecisely located moving camera

A probabilistic geometric model for 2D image line segments is first presented. We then propose a method using 2D segments, to accumulate evidence along an image sequence of a polyhedral scene. The proposed method degrades gracefully with camera location noise. The matched 2D segments are fused with an extended Kalman filter to reconstruct the scene structure. The correspondences are established using sequential data association. The matching function encodes the consistency between the 2D segments and the 3D segment computed from their fusion. The computation of a 3D segment from two 2D segments is overconstrained, and so using our model, the after-fusion consistency test can reject false matches even from two images, allowing the pruning of false matching hypotheses at early stages. Two examples are provided. The first determines a scene structure when the camera location is known precisely; the structure is then compared with that obtained by trinocular stereo. The proposed method improves 3D segment orientation computation and reduces the number of spurious segments. A second experiment demonstrates the graceful degradation of the performance, especially in orientation, with respect to the camera location noise.