Improved autonomous mobile ground control point robot collaborates with UAV to improve accuracy of agriculture remote sensing

Kha Dan; Collin Mcleod; Alex Thomasson

doi:10.1117/12.2622615

3 June 2022 Improved autonomous mobile ground control point robot collaborates with UAV to improve accuracy of agriculture remote sensing

Kha Dan, Collin Mcleod, Alex Thomasson

Author Affiliations +

Proceedings Volume 12114, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VII; 1211408 (2022) https://doi.org/10.1117/12.2622615
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

Conventional stationary ground control points (GCPs) provide calibration data to unmanned aerial vehicles (UAVs) for georeferencing and calibration of images. However, conventional GCPs are inefficient in a large field setup. An autonomous mobile GCP robot (AMGCP) combined with a collaboration algorithm to communicate with the UAV was developed to improve the efficiency and accuracy of the UAV-based data collection process. The first AMGCP lacks robustness in its navigation and structural stability. This research focuses on improving the previous version of the AMGCP by using ROS to implement simultaneous localization and mapping (SLAM) navigation architecture and enhancing the structural stability with a suspension system and four-wheel drive power train. Preliminary tests of the improved AMGCP showed a robust navigation system and improved structural stability. The first AMGCP showed superior performance for georeferencing and radiometric, height, and temperature calibration than conventional stationary GCPs. The improved AMGCP added an intelligent navigation system and structural stability.

Conference Presentation

Citation Download Citation

Kha Dan, Collin Mcleod, and Alex Thomasson "Improved autonomous mobile ground control point robot collaborates with UAV to improve accuracy of agriculture remote sensing", Proc. SPIE 12114, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VII, 1211408 (3 June 2022); https://doi.org/10.1117/12.2622615

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