3 accepted papers
Typical inference approaches that work with high-dimensional visual measurements use hand-engineered image features (e.g, SIFT) that require combinatorial data association, or predict only hidden state mean without considering its uncertainty and multi-modality aspects. We develop a novel approach t…
We develop a computationally efficient approach for evaluating the information theoretic term within belief space planning (BSP) considering both unfocused and focused problem settings, where uncertainty reduction of the entire system or only of chosen variables is of interest, respectively. State-o
We develop a novel approach for decision making under uncertainty in high-dimensional state spaces, considering both active unfocused and focused inference, where in the latter case reducing the uncertainty of only a subset of variables is of interest. State of the art approaches typically first cal…