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Remote Sens. 2013, 5(11), 5871-5906; doi:10.3390/rs5115871

Algorithmic Solutions for Computing Precise Maximum Likelihood 3D Point Clouds from Mobile Laser Scanning Platforms

1
Automation Group, School of Engineering and Science, Jacobs University Bremen gGmbH, Campus Ring 1, D-28759 Bremen, Germany
2
Informatics VII–Robotics and Telematics, University of Würzburg, Am Hubland, D-97074 Würzburg, Germany
*
Author to whom correspondence should be addressed.
Received: 15 August 2013 / Revised: 18 September 2013 / Accepted: 23 October 2013 / Published: 12 November 2013

Abstract

Mobile laser scanning puts high requirements on the accuracy of the positioning systems and the calibration of the measurement system. We present a novel algorithmic approach for calibration with the goal of improving the measurement accuracy of mobile laser scanners. We describe a general framework for calibrating mobile sensor platforms that estimates all configuration parameters for any arrangement of positioning sensors, including odometry. In addition, we present a novel semi-rigid Simultaneous Localization and Mapping (SLAM) algorithm that corrects the vehicle position at every point in time along its trajectory, while simultaneously improving the quality and precision of the entire acquired point cloud. Using this algorithm, the temporary failure of accurate external positioning systems or the lack thereof can be compensated for. We demonstrate the capabilities of the two newly proposed algorithms on a wide variety of datasets.
Keywords: mobile laser scanning; non-rigid registration; calibration; mapping; algorithms mobile laser scanning; non-rigid registration; calibration; mapping; algorithms
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Elseberg, J.; Borrmann, D.; Nüchter, A. Algorithmic Solutions for Computing Precise Maximum Likelihood 3D Point Clouds from Mobile Laser Scanning Platforms. Remote Sens. 2013, 5, 5871-5906.

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