Analytically-selected multi-hypothesis incremental MAP estimation
Author(s)Huang, Guoquan; Kaess, Michael; Roumeliotis, Stergios I.; Leonard, John Joseph
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In this paper, we introduce an efficient maximum a posteriori (MAP) estimation algorithm, which effectively tracks multiple most probable hypotheses. In particular, due to multimodal distributions arising in most nonlinear problems, we employ a bank of MAP to track these modes (hypotheses). The key idea is that we analytically determine all the posterior modes for the current state at each time step, which are used to generate highly probable hypotheses for the entire trajectory. Moreover, since it is expensive to solve the MAP problem sequentially over time by an iterative method such as Gauss-Newton, in order to speed up its solution, we reuse the previous computations and incrementally update the square-root informationmatrix at every time step, while batch relinearization is performed only periodically or as needed.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Mechanical Engineering
Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing
Institute of Electrical and Electronics Engineers (IEEE)
Huang, Guoquan, Michael Kaess, John J. Leonard, and Stergios I. Roumeliotis. “Analytically-Selected Multi-Hypothesis Incremental MAP Estimation.” 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (May 2013).
Author's final manuscript