Efficient Model-Aided Visual-Inertial Ego-Motion Estimation for Multirotor MAVs

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Abstract

When deployed onboard micro air vehicles (MAVs) with limited processing power, visual ego-motion estimation solutions face an efficiency-accuracy trade-off. This paper proposes an aerodynamic-model-aided approach that emphasizes time efficiency over estimation accuracy. A linear drag force model of propellers guarantees bounded estimation errors in the velocity components orthogonal to the shafts of propellers and the attitude relative to the gravity direction. Feature point correspondences are extracted from the monocular image stream to compute the relative heading angle and translational direction, which is fused with inertial measurements by an extended Kalman filter (EKF) in a loosely coupled manner. The proposed approach shows balanced performance in accuracy and efficiency. It also has robustness to situations where vision information becomes unavailable.
Original languageEnglish
Title of host publication14th annual international micro air vehicle conference and competition
EditorsD. Moormann
Pages93-100
Publication statusPublished - 2023
Event14th anual International Micro Air Vehicle Conference and Competition - Aachen , Germany
Duration: 11 Sept 202315 Sept 2023
Conference number: 14
https://2023.imavs.org/ (14th anual International Micro Air Vehicle Conference and Competition)

Conference

Conference14th anual International Micro Air Vehicle Conference and Competition
Abbreviated titleIMAV 2023
Country/TerritoryGermany
CityAachen
Period11/09/2315/09/23
Internet address

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