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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Maye, J. Furgale, P. Siegwart, R. |
| Copyright Year | 2013 |
| Description | Author affiliation: Autonomous Syst. Lab., ETH Zurich, Zurich, Switzerland (Maye, J.; Furgale, P.; Siegwart, R.) |
| Abstract | We present a generic algorithm for self calibration of robotic systems that utilizes two key innovations. First, it uses information theoretic measures to automatically identify and store novel measurement sequences. This keeps the computation tractable by discarding redundant information and allows the system to build a sparse but complete calibration dataset from data collected at different times. Second, as the full observability of the calibration parameters may not be guaranteed for an arbitrary measurement sequence, the algorithm detects and locks unobservable directions in parameter space using a truncated QR decomposition of the Gauss-Newton system. The result is an algorithm that listens to an incoming sensor stream, builds a minimal set of data for estimating the calibration parameters, and updates parameters as they become observable, leaving the others locked at their initial guess. Through an extensive set of simulated and real-world experiments, we demonstrate that our method outperforms state-of-the-art algorithms in terms of stability, accuracy, and computational efficiency. |
| Sponsorship | IEEE Intell. Transp. Syst. Soc. |
| Starting Page | 473 |
| Ending Page | 480 |
| File Size | 425316 |
| Page Count | 8 |
| File Format | |
| ISSN | 19310587 |
| e-ISBN | 9781467327558 |
| DOI | 10.1109/IVS.2013.6629513 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-06-23 |
| Publisher Place | Australia |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Calibration Robot sensing systems Matrix decomposition Observability Robot kinematics Vehicles |
| Content Type | Text |
| Resource Type | Article |
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