Data fusion and optimization in high dimensional processes
The application of techniques of Stochastic Data Fusion in estimation problems that involve high dimensional state vectors and the need to be performed under real-time constraints is a difficult task. In order to solve this type of problem, efficient techniques such versions of the EKF for high dimensional spaces, such as CEKF (Compressed Kalman Filter) are adequate tools. The adaptation of those techniques for Multi-core systems (such as GPU units) can be of relevant benefit. We investigate the implementation of expensive numerical processes such Stochastic Estimation (e.g. SLAM) and optimization approaches such as Dynamic Programming, by applying a .divide and conquer. policy, making the algorithms feasible to be performed in low cost parallel processing units (GPU).
Resulting Publications and Media (1)
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