Integrating Generic Sensor Fusion Algorithms with Sound State Representations through Encapsulation of Manifolds
Christoph Hertzberg, René Wagner, Udo Frese, Lutz Schröder
In Information Fusion, Elsevier, volume 14, number 1, pages 57-77, 2013.

Abstract :

Common estimation algorithms, such as least squares estimation or the Kalman filter, operate on a state in a state space S that is represented as a real-valued vector. However, for many quantities, most notably orientations in 3D, S is not a vector space, but a so-called manifold, i.e. it behaves like a vector space locally but has a more complex global topological structure. For integrating these quantities, several ad-hoc approaches have been proposed. Here, we present a principled solution to this problem where the structure of the manifold S is encapsulated by two operators, state displacement [+]:S x R^n --> S and its inverse [-]: S x S --> R^n. These operators provide a local vector-space view delta --> x [+] delta around a given state x. Generic estimation algorithms can then work on the manifold S mainly by replacing +/- with [+]/[-] where appropriate. We analyze these operators axiomatically, and demonstrate their use in least-squares estimation and the Unscented Kalman Filter. Moreover, we exploit the idea of encapsulation from a software engineering perspective in the Manifold Toolkit, where the [+]/[-] operators mediate between a "flat-vector" view for the generic algorithm and a "named-members" view for the problem specific functions.

Keywords :

Sensor fusion manifold state representation orientation

Files:

hertzberg_infus_11.pdf

Links:

http://www.sciencedirect.com/science/article/pii/S1566253511000571
http://arxiv.org/pdf/1107.1119v1


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