Robust Tracking and Structure from Motion with Sample Based Uncertainty Representation

Geometric reconstruction of the environment from images is critical in autonomous mapping and robot navigation. Geometric reconstruction involves feature tracking, i.e., locating corresponding image features in consecutive images, and structure from motion (SFM), i.e., recovering the 3-D structure of the environment from a set of correspondences between images. Although algorithms for feature tracking and structure from motion are wellestablished, their use in practical robot mobile applications is still difficult because of occluded features, non-smooth motion between frames, and ambiguous patterns in images. In this paper, we show how a sampling-based representation can be used in place of the traditional Gaussian representation of uncertainty. We show how sampling can be used for both feature tracking and SFM and we show how they are combined in this framework. The approach is exercised in the context of a mobile robot navigating through an outdoor environment with an omnidirectional camera.