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Forests 2015, 6(12), 4588-4606; doi:10.3390/f6124390

SLAM-Aided Stem Mapping for Forest Inventory with Small-Footprint Mobile LiDAR

1
GNSS Research Centre, Wuhan University, No.129 Luoyu Road, Wuhan 430079, China
2
Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute FGI, Geodeetinrinne 2, Kirkkonummi FI-02431, Finland
3
Key Laboratory of Quantitative Remote Sensing Information Technology, Academy of Opto-Electronics, Chinese Academy of Science, No.9 Deng Zhuang Nan Road, Beijing 100094, China
4
Department of Computer Science, University of Helsinki, Gustaf Hällströmin katu 2b, Helsinki FIN-00014, Finland
5
Department of Forest Sciences, University of Helsinki, Yliopistonkatu 4, Helsinki FI-00100, Finland
6
Department of Real Estate, Planning and Geoinformatics, Aalto University, P.O. Box 11000, Espoo, FI-00076, Finland
7
Helsinki Metropolia University of Applied Sciences, Construction and Real Estate Hubic, Helsinki FI-00100, Finland
*
Author to whom correspondence should be addressed.
Academic Editors: Joanne C. White and Eric J. Jokela
Received: 15 September 2015 / Revised: 24 November 2015 / Accepted: 8 December 2015 / Published: 17 December 2015
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Abstract

Accurately retrieving tree stem location distributions is a basic requirement for biomass estimation of forest inventory. Combining Inertial Measurement Units (IMU) with Global Navigation Satellite Systems (GNSS) is a commonly used positioning strategy in most Mobile Laser Scanning (MLS) systems for accurate forest mapping. Coupled with a tactical or consumer grade IMU, GNSS offers a satisfactory solution in open forest environments, for which positioning accuracy better than one decimeter can be achieved. However, for such MLS systems, positioning in a mature and dense forest is still a challenging task because of the loss of GNSS signals attenuated by thick canopy. Most often laser scanning sensors in MLS systems are used for mapping and modelling rather than positioning. In this paper, we investigate a Simultaneous Localization and Mapping (SLAM)-aided positioning solution with point clouds collected by a small-footprint LiDAR. Based on the field test data, we evaluate the potential of SLAM positioning and mapping in forest inventories. The results show that the positioning accuracy in the selected test field is improved by 38% compared to that of a traditional tactical grade IMU + GNSS positioning system in a mature forest environment and, as a result, we are able to produce a unambiguous tree distribution map. View Full-Text
Keywords: mobile laser scanning; IMU; GNSS; forest inventory; SLAM mobile laser scanning; IMU; GNSS; forest inventory; SLAM
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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. (CC BY 4.0).

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MDPI and ACS Style

Tang, J.; Chen, Y.; Kukko, A.; Kaartinen, H.; Jaakkola, A.; Khoramshahi, E.; Hakala, T.; Hyyppä, J.; Holopainen, M.; Hyyppä, H. SLAM-Aided Stem Mapping for Forest Inventory with Small-Footprint Mobile LiDAR. Forests 2015, 6, 4588-4606.

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