Motion Estimation by Hybrid Optical Flow Technology for UAV Landing in an Unvisited Area

Abstract

The capability of landing on previously unvisited areas is a fundamental challenge for an unmanned aerial vehicle (UAV). In this paper, we developed a vision-based motion estimation as an aid to improve landing performance. As an alternative to the common scenarios accompanying by external infrastructures or well-defined marker, the proposed hybrid framework can successfully land on a new area without any prior information about guiding marks. The implementation was based on the optical flow technique associated with a multi-scale strategy to overcome the decreasing field-of-view during the UAV descending. Compared with a commercial Global Positioning System (GPS) through a sequence of flight trials, the vision-aided scheme can effectively minimize the possible sensing error, thus, leading to a more accurate result. Moreover, this work has potential to integrate the fast-growing image learning process and yields more practical versatility for UAV applications in the future.

Learning-based risk assessment and motion estimation by vision for unmanned aerial vehicle landing in an unvisited area

“…In addition, the experimental results indicated that we successfully carried out the vision-based autonomous landing with a positioning error of ∼0.1 m. The detailed discussion of landing performance can be found in our previous work. 21 To enable the vision system more completely in UAV applications, more effort shall be paid to tackle the problem of visually retrieving depth information. Moreover, online learning would make the UAV more versatile by autonomously selecting a safe landing spot.…”

“…The vision system generated the guidance information with the flight data from the control system. Following our previous work, 21 we dynamically adopted the Gunnar-Farnebäck algorithm 27 and PC method [28][29][30] to obtain the velocity and position, respectively. These two algorithms worked together to combine the advantages of dense and sparse optical flow in terms of accuracy and robustness.…”

“…Our team aims to develop a fully vision-based system for UAV landings in a previously unvisited environment. Resuming our previous work on vision-based landing motion estimation, 21 we further integrated the vision system with the learning algorithm. The major function of the proposed system is to classify the obstacle appearance on the ground and provide an accurate measure of motion during the landing.…”

Retrieval of Ice Samples Using the Ice Drone

“…Future iterations of IceDrone could include onboard image processing algorithms that can autonomously select scientifically interesting sampling sites and then rank them based on the presence/absence of obstacles and the slope of the surrounding surface. We also note that autonomous landing site selection on uneven and unprepared terrain remains an active area of research (e.g., Forster et al, 2015;Stolz et al, 2018;Cheng et al, 2019;Pestana et al, 2019) and was well-beyond the scope of the undergraduate student projects featured here. Stereoscopic first-person-view (FPV) goggles may be a more practical short-term solution (e.g., Smolyankskiy and Gonzalez-Franco, 2017) to enable semi-autonomous operations.…”

Autonomous First Response Drone-Based Smart Rescue System for Critical Situation Management in Future Wireless Networks

“…This means low bandwidth consumption. The complexity of a LoRa receiver is also reduced considerably due to the similarity of the offset in time and frequency between the sender and receiver [22]. Moreover, there is minimum interference and low power consumption.…”