19 May 2016 Optimal rotation sequences for active perception
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One major objective of autonomous systems navigating in dynamic environments is gathering information needed for self localization, decision making, and path planning. To account for this, such systems are usually equipped with multiple types of sensors. As these sensors often have a limited field of view and a fixed orientation, the task of active perception breaks down to the problem of calculating alignment sequences which maximize the information gain regarding expected measurements. Action sequences that rotate the system according to the calculated optimal patterns then have to be generated. In this paper we present an approach for calculating these sequences for an autonomous system equipped with multiple sensors. We use a particle filter for multi- sensor fusion and state estimation. The planning task is modeled as a Markov decision process (MDP), where the system decides in each step, what actions to perform next. The optimal control policy, which provides the best action depending on the current estimated state, maximizes the expected cumulative reward. The latter is computed from the expected information gain of all sensors over time using value iteration. The algorithm is applied to a manifold representation of the joint space of rotation and time. We show the performance of the approach in a spacecraft navigation scenario where the information gain is changing over time, caused by the dynamic environment and the continuous movement of the spacecraft
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David Nakath, David Nakath, Carsten Rachuy, Carsten Rachuy, Joachim Clemens, Joachim Clemens, Kerstin Schill, Kerstin Schill, "Optimal rotation sequences for active perception", Proc. SPIE 9872, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2016, 987204 (19 May 2016); doi: 10.1117/12.2223027; https://doi.org/10.1117/12.2223027


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