The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B1-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2022, 197–205, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-197-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2022, 197–205, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-197-2022
 
30 May 2022
30 May 2022

SLAM AND INS BASED POSITIONAL ACCURACY ASSESSMENT OF NATURAL AND ARTIFICIAL OBJECTS UNDER THE FOREST CANOPY

J. Chudá1, R. Kadlečík2, M. Mokroš1,3, T. Mikita4, J. Tuček2, and F. Chudý2 J. Chudá et al.
  • 1Department of Forest Harvesting, Logistics and Ameliorations, Faculty of Forestry, Technical University in Zvolen, Zvolen, Slovakia
  • 2Department of Forest Resource Planning and Informatics, Faculty of Forestry, Technical University in Zvolen, Zvolen, Slovakia
  • 3Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Prague, Czech Republic
  • 4Department of Forest Management and Applied Geoinformatics, Faculty of Forestry and Wood Technology, Mendel University in Brno, Zemědělská 3, 61300 Brno, Czech Republic

Keywords: SLAM, IMU, object position, trajectory, forest

Abstract. The positional accuracy derived from the outputs of carried integrated devices was evaluated in this study. For data collection, the inertial navigation system (INS) SPAN NovAtel and a handheld mobile laser scanner GeoSLAM ZEB Horizon which uses simultaneous localization and mapping technology (SLAM) were utilized for data collection. The accuracy was assessed on the set of reference objects located under the forest canopy, which were measured via a traditional field survey (the methods of geodesy). In the results of this study the high potential of the devices and the application of data collection methods into forestry practice were pointed out. In our research, when the horizontal position of artificial entities was evaluated the average RMSE of 0.26 m, and the average positional RMSE of the derived natural objects (trees) was 0.09 m, both extracted from SLAM. The horizontal positional accuracy of trajectories with RMSE of 9.93 m (INS) and 0.40 m (SLAM) were accomplished.