3D-MFDNN: Three-dimensional multi-feature descriptors combined deep neural network for vegetation segmentation from airborne laser scanning data

Singh, Dheerendra Pratap; Yadav, Manohar

doi:10.1016/j.measurement.2023.113465

Cited by 5 publications

(1 citation statement)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Airborne Laser Scanning (ALS), has emerged as a transformative force in UAV landing site selection (Singh and Yadav, 2023). UAVs equipped with LiDAR sensors can capture highly detailed three-dimensional datasets.…”

Section: Introduction 11 Motivationsmentioning

confidence: 99%

Suitable Landing Site Selection for Unmanned Aerial Vehicles Using Airborne Laser Scanning Point Cloud

Singh,

Yadav

2024

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. In the context of autonomous landing for unmanned aerial vehicles (UAVs), selecting a suitable landing site is crucial. This research presents a new method for automatically identifying safe landing sites based on point cloud data acquired through an airborne laser scanning (ALS) system. The proposed approach begins by detecting flat regions using principal component analysis (PCA) and region-growing algorithms. Subsequently, a terrain complexity assessment is conducted through plane fitting using an enhanced progressive sample consensus (PROSAC) algorithm. This assessment assists in identifying the most suitable landing site within a specified landing zone. The method's effectiveness is demonstrated through experiments conducted on two distinct natural terrains from the DALE dataset. The results show that the proposed approach can accurately classify landing zones and identify preferred sites that meet safety criteria. This study's findings underscore the proposed method's effectiveness and feasibility in improving the safety and reliability of autonomous UAV landings.

show abstract

Section: Introduction 11 Motivationsmentioning

confidence: 99%