Estimating camera and object translation in the presence of camera rotation

This paper deals with stereo camera-based estimation of sensor translation in the presence of modest sensor rotation and moving objects. It also deals with the estimation of object translation from a moving sensor. The approach is based on Gabor filters, direct passive navigation, and Kalman filters.Three difficult problems associated with the estimation of motion from an image sequence are solved. (1) The temporal correspondence problem is solved using multi-scale prediction and phase gradients. (2) Segmentation of the image measurements into groups belonging to stationary and moving objects is achieved using the Mahalanobis distance. (3) Compensation for sensor rotation is achieved by internally representing the inter-frame (short-term) rotation in a rigid-body model. These three solutions possess a circular dependency, forming a cycle of perception. A seeding process is developed to correctly initialize the cycle. An additional complication is the translation-rotation ambiguity that sometimes exists when sensor motion is estimated from an image velocity field. Temporal averaging using Kalman filters reduces the effect of motion ambiguities. Experimental results from real image sequences confirm the utility of the approach.Financial support from the Natural Science and Engineering Research Council (NSERC) of Canada is acknowledged.