A Generalized Kalman Filter Augmented Deep-Learning based Approach for Autonomous Landing in MAVs

Mathur, Pranay; Jangir, Yash; Goveas, Neena

doi:10.1109/iria53009.2021.9588758

Cited by 3 publications

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is a successful approach which enhances the system's performance and energy efficiency. [5] proposes a two-stage training system to detect landing areas. A CNN trained over synthetic images is used in the first stage, then a custom Kalman Filter controller is used to have an accurate control during landing and approach the landing site.…”

Section: Related Workmentioning

confidence: 99%

Design and Deployment of an Efficient Landing Pad Detector

Albanese

Taccioli

Apicella

et al. 2022

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

Robust landing pad detection plays a major role in Autonomous Unmanned Aerial Vehicles (UAVs). This problem can be approached using deep neural networks for vision-based inference. However, the full integration of deep learning algorithms into the small UAVs is still challenging for their limited resources. This paper presents a landing pad detection pipeline based on a revisited version MobileNetV3-Small. The proposed architecture inherits robustness from the general-purpose version but limits the computational cost significantly thanks to a set of design criteria aimed to limit hardware requirements. Experimental results confirm that the proposed network compares favorably with a lightweight general-purpose object detector in terms of accuracy/computational cost trade-off. The system is also deployed on a commercial general-purpose microcomputer confirming that satisfactory performance can be obtained on general-purpose embedded architectures.

show abstract

Section: Related Workmentioning

confidence: 99%