Abstract
Advances in unmanned aerial vehicles (UAV) and especially micro aerial vehicle (MAV) technology together with increasing quality and decreasing price of imaging devices have resulted in growing use of MAVs in photogrammetry. The practicality of MAV mapping is seriously enhanced with the ability to determine parameters of exterior orientation (EO) with sufficient accuracy, in both absolute and relative senses (change of attitude between successive images). While differential carrier phase GNSS satisfies cm-level positioning accuracy, precise attitude determination is essential for both direct sensor orientation (DiSO) and integrated sensor orientation (ISO) in corridor mapping or in block configuration imaging over surfaces with low texture. Limited cost, size, and weight of MAVs represent limitations on quality of onboard navigation sensors and puts emphasis on exploiting full capacity of available resources. Typically short flying times (10-30 minutes) also limit the possibility of estimating and/or correcting factors such as sensor misalignment and poor attitude initialization of inertial navigation system (INS). This research aims at increasing the accuracy of attitude determination in both absolute and relative senses with no extra sensors onboard. In comparison to classical INS/GNSS setup, novel approach is presented here to integrated state estimation, in which vehicle dynamic model (VDM) is used as the main process model. Such system benefits from available information from autopilot and physical properties of the platform in enhancing performance of determination of trajectory and parameters of exterior orientation consequently. The navigation system employs a differential carrier phase GNSS receiver and a micro electro-mechanical system (MEMS) grade inertial measurement unit (IMU), together with MAV control input from autopilot. Monte-Carlo simulation has been performed on trajectories for typical corridor mapping and block imaging. Results reveal considerable reduction in attitude errors with respect to conventional INS/GNSS system, in both absolute and relative senses. This eventually translates into higher redundancy and accuracy for photogrammetry applications.
Highlights
A novel autonomous navigation for unmanned aerial vehicles based on vehicle dynamic model recently introduced by the authors in (Khaghani and Skaloud, 2016), focused on reducing navigation error in GNSS outage conditions
A shortened introduction on principles of the proposed method is presented in Section 2 and Section 3, while the main part of the contribution is presented in Section 4 and focuses on results and discussions related to attitude determination accuracy in block and corridor mapping scenarios
A novel method to perform autonomous navigation and sensor integration for unmanned aerial vehicles (UAV) was recently introduced by the authors
Summary
A novel autonomous navigation for unmanned aerial vehicles based on vehicle dynamic model recently introduced by the authors in (Khaghani and Skaloud, 2016), focused on reducing navigation error in GNSS outage conditions. This contribution explores the possibility of using that method in presence of high accuracy GNSS solution to improve attitude determination accuracy for photogrammetry applications, compared to conventional INS/GNSS integration. A shortened introduction on principles of the proposed method is presented in Section 2 and Section 3, while the main part of the contribution is presented in Section 4 and focuses on results and discussions related to attitude determination accuracy in block and corridor mapping scenarios
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