Matthies et al.1987 : Using known camera motion to estimate depth from image sequences is important in robotics applications such as navigation and manipulation. In this paper, we introduce a new pixel-based (iconic) algorithm that estimates depth from an image sequence and incrementally refines its estimate over time. We also present a feature-based version of the algorithm which is used for comparison. We compare the performance of both approaches mathematically, with quantitative experiments using images of a flat scene, and with qualitative experiments using images of a realistic outdoor scene model. The results show that the method is an effective way to extract depth from lateral camera translations. Our approach can be extended to incorporate general motion and to integrate other sources of information such as stereo. The algorithms which we have developed, which combine Kalman filtering with iconic descriptions of depth, can thus serve as a useful and general framework for low-level dynamic vision.