The problem of finding the rotation and the translation between two sets of corresponded points is known as the rigid transformation estimation problem. It plays a crucial role in many robotic applications such as “simultaneous localization and mapping” (SLAM), surface reconstruction, and inertial sensor calibration. The most widely used solution to this problem is based on performing the singular value decomposition (SVD) over a derived data matrix. A drawback of the SVD method is that it is a least-squares method and thus may fail to take into account the anisotropic and/or correlated noises, which often present in practical applications. A natural variation is to add a matrix weight to the least-squares problem to balance the estimation errors in different measurement directions. However, it becomes difficult to write down a closed form solution in this setup. In this paper, an efficient algorithm is presented to estimate the rigid transformation with correlated observations. The effectiveness of the proposed method is experimentally demonstrated on two robotic applications, namely the point set registration and the inertial sensor localization.
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ASME 2015 Dynamic Systems and Control Conference
October 28–30, 2015
Columbus, Ohio, USA
Conference Sponsors:
- Dynamic Systems and Control Division
ISBN:
978-0-7918-5725-0
PROCEEDINGS PAPER
Estimating Rigid Transformation With Correlated Observations Available to Purchase
Chung-Yen Lin,
Chung-Yen Lin
University of California, Berkeley, CA
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Masayoshi Tomizuka
Masayoshi Tomizuka
University of California, Berkeley, CA
Search for other works by this author on:
Chung-Yen Lin
University of California, Berkeley, CA
Masayoshi Tomizuka
University of California, Berkeley, CA
Paper No:
DSCC2015-9672, V002T23A002; 9 pages
Published Online:
January 12, 2016
Citation
Lin, C, & Tomizuka, M. "Estimating Rigid Transformation With Correlated Observations." Proceedings of the ASME 2015 Dynamic Systems and Control Conference. Volume 2: Diagnostics and Detection; Drilling; Dynamics and Control of Wind Energy Systems; Energy Harvesting; Estimation and Identification; Flexible and Smart Structure Control; Fuels Cells/Energy Storage; Human Robot Interaction; HVAC Building Energy Management; Industrial Applications; Intelligent Transportation Systems; Manufacturing; Mechatronics; Modelling and Validation; Motion and Vibration Control Applications. Columbus, Ohio, USA. October 28–30, 2015. V002T23A002. ASME. https://doi.org/10.1115/DSCC2015-9672
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