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Proceedings Paper

Distance error correction for time-of-flight cameras
Author(s): Peter Fuersattel; Christian Schaller; Andreas Maier; Christian Riess
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Paper Abstract

The measurement accuracy of time-of-flight cameras is limited due to properties of the scene and systematic errors. These errors can accumulate to multiple centimeters which may limit the applicability of these range sensors. In the past, different approaches have been proposed for improving the accuracy of these cameras. In this work, we propose a new method that improves two important aspects of the range calibration. First, we propose a new checkerboard which is augmented by a gray-level gradient. With this addition it becomes possible to capture the calibration features for intrinsic and distance calibration at the same time. The gradient strip allows to acquire a large amount of distance measurements for different surface reflectivities, which results in more meaningful training data. Second, we present multiple new features which are used as input to a random forest regressor. By using random regression forests, we circumvent the problem of finding an accurate model for the measurement error. During application, a correction value for each individual pixel is estimated with the trained forest based on a specifically tailored feature vector. With our approach the measurement error can be reduced by more than 40% for the Mesa SR4000 and by more than 30% for the Microsoft Kinect V2. In our evaluation we also investigate the impact of the individual forest parameters and illustrate the importance of the individual features.

Paper Details

Date Published: 26 June 2017
PDF: 10 pages
Proc. SPIE 10334, Automated Visual Inspection and Machine Vision II, 103340V (26 June 2017); doi: 10.1117/12.2271775
Show Author Affiliations
Peter Fuersattel, Friedrich-Alexander-Univ. Erlangen-Nürnberg (Germany)
Metrilus GmbH (Germany)
Christian Schaller, Metrilus GmbH (Germany)
Andreas Maier, Friedrich-Alexander-Univ. Erlangen-Nürnberg (Germany)
Christian Riess, Friedrich-Alexander Univ. Erlangen-Nürnberg (Germany)

Published in SPIE Proceedings Vol. 10334:
Automated Visual Inspection and Machine Vision II
Jürgen Beyerer; Fernando Puente León, Editor(s)

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