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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.
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.
Original language | English |
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Title of host publication | Proceedings of the 34th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2021) |
Place of Publication | St. Louis, Missouri |
Pages | 3250-3262 |
Number of pages | 13 |
ISBN (Electronic) | 9780936406299 |
DOIs | |
Publication status | Published - Sept 2021 |
Publication series
Name | Proceedings of the 34th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2021 |
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ASJC Scopus subject areas
- Software
- Electrical and Electronic Engineering
- Computer Science Applications
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Dive into the research topics of 'Performance Analysis of GNSS/INS/VO/Odometry Sensor Fusion Algorithms for Tracked Agricultural Vehicles'. Together they form a unique fingerprint.Activities
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Performance Analysis of GNSS/INS/VO/Odometry Sensor Fusion Algorithms for Tracked Agricultural Vehicles
Eva Maria Reitbauer (Speaker)
20 Sept 2021 → 24 Sept 2021Activity: Talk or presentation › Talk at conference or symposium › Science to science
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34th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2021)
Eva Maria Reitbauer (Participant)
20 Sept 2021 → 24 Sept 2021Activity: Participation in or organisation of › Conference or symposium (Participation in/Organisation of)