Experimental evaluation of a real-time GPU-based pose estimation system for autonomous landing of rotary wings UAVs

Benini, Alessandro; Rutherford, Matthew J.; Valavanis, Kimon P.

doi:10.1007/s11768-018-7297-9

Cited by 5 publications

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed pad is composed of patterns of different sizes, permitting their detection from high as well as very low altitudes. For the application purpose, the main advantage of the approach in Benini et al 14 is the use of a GPU for detecting the marker in order to achieve onboard high-frequency pose estimation and marker detection. The use of the GPU allows fast image analysis even in cluttered environments that otherwise would be difficult to achieve with CPU-based algorithms.…”

Section: Previous Workmentioning

confidence: 99%

A hierarchical vision-based localization of rotor unmanned aerial vehicles for autonomous landing

Yuan

Xiao

Xiu

et al. 2018

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

The vision-based localization of rotor unmanned aerial vehicles for autonomous landing is challenging because of the limited detection range. In this article, to extend the vision detection and measurement range, a hierarchical vision-based localization method is proposed for unmanned aerial vehicle autonomous landing. In such a hierarchical framework, the landing is defined into three phases: “Approaching,”“Adjustment,” and “Touchdown,” in which visual artificial features at different scales can be detected from the designed object pattern for unmanned aerial vehicle pose recovery. The corresponding feature detection and pose estimation algorithms are also presented. In the end, typical simulation and field experiments have been carried out to illustrate the proposed method. The results show that our hierarchical vision-based localization has the ability to a consecutive unmanned aerial vehicle localization in a wider working range from far to near, which is significant for autonomous landing.

show abstract

Section: Previous Workmentioning

confidence: 99%