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Multi-Sensor Platform for Indoor Mobile Mapping: System Calibration and Using a Total Station for Indoor Applications
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Remote Sens. 2013, 5(12), 6611-6646;

Indoor Localization Algorithms for an Ambulatory Human Operated 3D Mobile Mapping System

Video and Image Processing Lab, University of California, Berkeley, CA 94720, USA
Authors to whom correspondence should be addressed.
Received: 18 October 2013 / Revised: 25 November 2013 / Accepted: 28 November 2013 / Published: 3 December 2013
(This article belongs to the Special Issue Advances in Mobile Laser Scanning and Mobile Mapping)
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Indoor localization and mapping is an important problem with many applications such as emergency response, architectural modeling, and historical preservation. In this paper, we develop an automatic, off-line pipeline for metrically accurate, GPS-denied, indoor 3D mobile mapping using a human-mounted backpack system consisting of a variety of sensors. There are three novel contributions in our proposed mapping approach. First, we present an algorithm which automatically detects loop closure constraints from an occupancy grid map. In doing so, we ensure that constraints are detected only in locations that are well conditioned for scan matching. Secondly, we address the problem of scan matching with poor initial condition by presenting an outlier-resistant, genetic scan matching algorithm that accurately matches scans despite a poor initial condition. Third, we present two metrics based on the amount and complexity of overlapping geometry in order to vet the estimated loop closure constraints. By doing so, we automatically prevent erroneous loop closures from degrading the accuracy of the reconstructed trajectory. The proposed algorithms are experimentally verified using both controlled and real-world data. The end-to-end system performance is evaluated using 100 surveyed control points in an office environment and obtains a mean accuracy of 10 cm. Experimental results are also shown on three additional datasets from real world environments including a 1500 meter trajectory in a warehouse sized retail shopping center. View Full-Text
Keywords: backpack; mobile mapping; SLAM; indoor localization backpack; mobile mapping; SLAM; indoor localization
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Corso, N.; Zakhor, A. Indoor Localization Algorithms for an Ambulatory Human Operated 3D Mobile Mapping System. Remote Sens. 2013, 5, 6611-6646.

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