Genetic Fuzzy based Target Geo-localization using Unmanned Aerial Systems for Firefighting Applications

Kukreti, Sarthak; Kumar, Manish; Cohen, Kelly

doi:10.2514/6.2018-2136

Cited by 7 publications

(1 citation statement)

References 17 publications

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“…One type relies on high-precision attitude sensors, Real-Time Kinematics (RTK), and visual information to calculate the actual geographic coordinates of the target.Madison [1][2][3] modeled the localization of moving targets by unmanned aerial vehicles (UAVs) as a linear system and introduced the Kalman filter (KF) to estimate target states, thereby improving localization accuracy. However, at a height of 100 meters, UAVs equipped with conventional sensors exhibited a localization error exceeding 20 meters.Kukreti [4][5] employed the Extended Kalman Filter (EKF) to estimate target position and motion states, achieving detection and pose estimation of moving targets. This was simulated solely through MATLAB and did not undergo practical flight experiments.…”

Section: Introductionmentioning

confidence: 99%

Unmanned Aerial Vehicle Based Power Pole Localization Using Image Stitching

Zhang,

Guo,

et al. 2024

E3S Web of Conf.

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When facing natural disasters, power transmission and distribution towers are prone to tilting, collapsing, and other situations, which can easily lead to line faults and subsequently cause power outage incidents. In order to quickly and accurately locate abnormal towers and restore power supply promptly, the power maintenance department typically employs unmanned aerial vehicles (UAVs) for post-disaster inspections of power line towers. Traditional methods for UAV ground target localization mainly rely on the UAV’s own position, attitude, and onboard sensor information to indirectly calculate the target’s position. This method is influenced by the Global Positioning System (GPS) and Inertial Navigation System (INS), relying on high-precision sensors. To achieve precise positioning of power line towers with simplified equipment on UAVs, this paper proposes a visual passive localization method based on ground control points. Addressing the sparse distribution of ground control points, a solution based on image stitching is introduced to overcome difficulties in localization caused by insufficient ground control points.

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