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Sensors 2010, 10(11), 10356-10375; doi:10.3390/s101110356

A General Purpose Feature Extractor for Light Detection and Ranging Data

*  and
Department of Computer Science Engineering, University of Michigan, 2260 Hayward St., Ann Arbor, MI 48109, USA
* Author to whom correspondence should be addressed.
Received: 17 September 2010 / Revised: 7 October 2010 / Accepted: 30 October 2010 / Published: 17 November 2010
(This article belongs to the Special Issue Intelligent Sensors - 2010)
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Feature extraction is a central step of processing Light Detection and Ranging (LIDAR) data. Existing detectors tend to exploit characteristics of specific environments: corners and lines from indoor (rectilinear) environments, and trees from outdoor environments. While these detectors work well in their intended environments, their performance in different environments can be poor. We describe a general purpose feature detector for both 2D and 3D LIDAR data that is applicable to virtually any environment. Our method adapts classic feature detection methods from the image processing literature, specifically the multi-scale Kanade-Tomasi corner detector. The resulting method is capable of identifying highly stable and repeatable features at a variety of spatial scales without knowledge of environment, and produces principled uncertainty estimates and corner descriptors at same time. We present results on both software simulation and standard datasets, including the 2D Victoria Park and Intel Research Center datasets, and the 3D MIT DARPA Urban Challenge dataset.
Keywords: SLAM; LIDARs; feature detection; uncertainty estimates; descriptors SLAM; LIDARs; feature detection; uncertainty estimates; descriptors
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Li, Y.; Olson, E.B. A General Purpose Feature Extractor for Light Detection and Ranging Data. Sensors 2010, 10, 10356-10375.

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