Abstract
Moving Horizon Estimation (MHE) offers multiple advantages over Kalman Filters when it comes to the localization of drones. However, due to the high computational cost, they can not be used on Micro Air Vehicles (MAVs) with limited computational power. We have previously shown, that with a few assumptions and simplifications, MHE can be made more efficient while retaining good localization performance. In this paper, we present two additional improvements: the introduction of dynamic step sizes to the gradient descent algorithm, which leads to a significant increase in robustness, and the use of switching variables for outlier rejection, which further reduces the computational load. Both improvements are implemented and assessed in simulation and experiments. Using dynamic step sizes makes it possible to reliably use the estimator on board of a real drone, and the use of Newton’s method specifically opens the option to add different types of measurements. The new outlier rejection method on the other hand is shown to reduce the computational load significantly while having no big impact on accuracy.
Original language | English |
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Title of host publication | 14th annual international micro air vehicle conference and competition |
Editors | D. Moormann |
Pages | 86-92 |
Publication status | Published - 2023 |
Event | 14th anual International Micro Air Vehicle Conference and Competition - Aachen , Germany Duration: 11 Sept 2023 → 15 Sept 2023 Conference number: 14 https://2023.imavs.org/ (14th anual International Micro Air Vehicle Conference and Competition) |
Conference
Conference | 14th anual International Micro Air Vehicle Conference and Competition |
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Abbreviated title | IMAV 2023 |
Country/Territory | Germany |
City | Aachen |
Period | 11/09/23 → 15/09/23 |
Internet address |
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