Dynamic structure from motion based on nonlinear adaptive observers

Structure and motion estimation from long image sequences is a an important and difficult problem in computer vision. We propose a novel approach based on nonlinear and adaptive observers based on a dynamic model of the motion. The estimation of the three-dimensional position and velocity of the camera as well as the three-dimensional structure of the scene is done by observing states and parameters of a nonlinear dynamic system, containing a perspective transformation in the output equation, often referred to as a perspective dynamic system. An advantage of the proposed method is that it is filter-based, i.e. it provides an estimate of structure and motion at each time instance, which is then updated based on a novel image in the sequence. The observer demonstrates a trade-off compared to a more computer vision oriented approach, where no specific assumptions regarding the motion dynamics are required, but instead additional feature points are needed. Finally, the performance of the proposed method is shown in simulated experiments.