Performance Analysis of GNSS/INS/VO/Odometry Sensor Fusion Algorithms for Tracked Agricultural Vehicles

Eva Reitbauer, Christoph Schmied

Abstract: In the last decade, high-accuracy GNSS has played an increasingly important role in the agricultural sector. However, when used as a stand-alone sensor for positioning, GNSS cannot meet the requirements of autonomous agricultural machinery. To ensure high availability, robustness and increased accuracy of position and attitude, new sensor fusion concepts tailored to agricultural applications must be developed. The paper presents two sensor fusion algorithms for tracked agricultural vehicles. The first one is an error-state cascaded integration which uses GNSS, Odometry, and Visual Odometry (VO), paired with a point cloud registration algorithm called Normal distributions Transform (NDT), as aiding sensors and the IMU as reference sensor. The second consists of two local error-state filters, one for GNSS/INS fusion and the other for fusing VO/NDT and Odometry, where the result of the local filters is combined in a snapshot fusing algorithm. To find out which integration architecture is best suited for tracked agricultural vehicles like compost turners, the filters are tested at a composting site and evaluated regarding their achievable real-time accuracy for position and attitude. The results show that both filters achieve sub-decimetre accuracy for the positioning solution, but the cascaded integration architecture is more robust against outliers.
Published in: Proceedings of the 34th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2021)
September 20 - 24, 2021
Union Station Hotel
St. Louis, Missouri
Pages: 3250 - 3262
Cite this article: Reitbauer, Eva, Schmied, Christoph, "Performance Analysis of GNSS/INS/VO/Odometry Sensor Fusion Algorithms for Tracked Agricultural Vehicles," Proceedings of the 34th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2021), St. Louis, Missouri, September 2021, pp. 3250-3262.
https://doi.org/10.33012/2021.18053
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