Temporal accumulation of oriented visual features

In this paper we present a framework for accumulating on-line a model of a moving object (e.g., when manipulated by a robot). The proposed scheme is based on Bayesian filtering of local features, filtering jointly position, orientation and appearance information. The work presented here is novel in two aspects: first, we use an estimation mechanism that updates iteratively not only geometrical information, but also appearance information. Second, we propose a probabilistic version of the classical n-scan criterion that allows us to select which features are preserved and which are discarded, while making use of the available uncertainty model.The accumulated representations have been used in three different contexts: pose estimation, robotic grasping, and driver assistance scenario.

Research Highlights
► On-line accumulation of moving objects models.
► Probabilistic formulation of n-scan criterion.
► Unscented Kalman filtering for feature full-pose and appearance.