Integrating Generic Sensor Fusion Algorithms with Sound State Representation through Encapsulation of Manifolds
| Type of publication: | Article |
| Citation: | hertzbergIF10 |
| Journal: | Information Fusion |
| Volume: | 14 |
| Number: | 1 |
| Year: | 2013 |
| Pages: | 57--77 |
| Note: | Available online 14 September 2011 |
| ISSN: | 1566-2535 |
| URL: | http://www.sciencedirect.com/s... |
| 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. |
| Userfields: | bdsk-url-1={http://www.sciencedirect.com/science/article/pii/S1566253511000571}, pdfurl={http://arxiv.org/pdf/1107.1119v1}, project={A7-FreePerspective}, status={Reviewed}, |
| Keywords: | Sensor fusion manifold state representation orientation |
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