Estimating vector magnitude from its direction and derivative, with application to bearing-only SLAM filter problem
| Autor: | Tor Arne Johansen, Elias Bjorne, Jeff Delaune |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Observer (quantum physics) Magnitude (mathematics) 02 engineering and technology Filter (signal processing) Derivative Acceleration 020901 industrial engineering & automation Unit vector Position (vector) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Algorithm Mathematics Euclidean vector |
| Zdroj: | CDC 2018-December I E E E Conference on Decision and Control. Proceedings |
| ISSN: | 1353-1360 |
| Popis: | For some problems, such as monocular visual odometry (VO), vector measurements are given with unknown magnitude. In VO, the magnitude can be found by recognizing features with known position, or with an extra sensor such as an altimeter. This article presents a nonlinear observer that uses the derivative of the vector as an additional measurement for estimating the magnitude of a vector. For the VO example, this means that the velocity can be estimated by fusing the normalized velocity vector with acceleration measurements. The observer exploits the fact that the dynamics of the normalized vector is dependent on the magnitude of the vector. The observer employs methods from nonlinear/adaptive estimation; filters the unit vector on the unit sphere, and retrieves the magnitude of the vector. The observer is shown to be uniformly semi-globally asymptotically (USGAS) stable and uniformly exponentially stable (UES) in a defined region. The observer is applied to the bearing-only SLAM filter problem as an example. © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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