Autonomous target localization using quadrotor

Hou, Yun Hai; Yu, Changbin

doi:10.1109/ccdc.2014.6852285

Cited by 10 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A combination of 2D laser range finder with a monocular camera can be used for the localization. 30 Although the maximum deviation recorded using this method was about 13% from the actual measurement. This may be due to the not so robust target detection process employed in the paper.…”

Section: Literature Reviewmentioning

confidence: 78%

See 1 more Smart Citation

A novel UAV-integrated deep network detection and relative position estimation approach for weeds

Abdulsalam

Ahiska

Aouf

2023

Proceedings of the Institution of Mechanical Engineers, Part G:

View full text Add to dashboard Cite

This paper aims at presenting a novel monocular vision–based approach for drones to detect multiple type of weeds and estimate their positions autonomously for precision agriculture applications. The methodology is based on classifying and detecting the weeds using a proposed deep neural network architecture, named fused-YOLO on the images acquired from a monocular camera mounted on the unmanned aerial vehicle (UAV) following a predefined elliptical trajectory. The detection/classification is complemented by a new estimation scheme adopting unscented Kalman filter (UKF) to estimate the exact location of the weeds. Bounding boxes are assigned to the detected targets (weeds) such that the centre pixels of the bounding box will represent the centre of the target. The centre pixels are extracted and converted into world coordinates forming azimuth and elevation angles from the target to the UAV, and the proposed estimation scheme is used to extract the positions of the weeds. Experiments were conducted both indoor and outdoor to validate this integrated detection/classification/estimation approach. The errors in terms of misclassification and mispositioning of the weeds estimation were minimum, and the convergence of the position estimation results was short taking into account the affordable platform with cheap sensors used in the experiments.

show abstract

Section: Literature Reviewmentioning

confidence: 78%

“…The crowded literature on target localization can be grouped according to the platform used, 30,31 or the sensors employed 18,32 or the estimation model studied. 31,33 The main aim for all is to maximize the localization accuracy and minimize the time required.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel UAV-integrated deep network detection and relative position estimation approach for weeds

Abdulsalam

Ahiska

Aouf

2023

Proceedings of the Institution of Mechanical Engineers, Part G:

View full text Add to dashboard Cite

show abstract

“…In the environment that is not covered by GPS, IJECE ISSN: 2088-8708  Inertial Navigation for Quadrotor Using Kalman Filter with Drift Compensation (Lasmadi) 2597 navigation or localization of a UAV can only depend on the IMU (Inertial Measurement Unit) and the exteroceptive sensor such as the laser range finder (LRF) and the camera. The systems use the LRF sensor for UAV navigation system has been carried out in [21], [24], and [25]. The LRF sensor has a limitation of the range that may lead to less accurate position measurement [19].…”

Section: Introductionmentioning

confidence: 99%

Inertial Navigation for Quadrotor Using Kalman Filter with Drift Compensation

Lasmadi¹,

Cahyadi²,

Herdjunanto³

et al. 2017

IJECE

View full text Add to dashboard Cite

The main disadvantage of an Inertial Navigation System is a low accuracy due to noise, bias, and drift error in the inertial sensor. This research aims to develop the accelerometer and gyroscope sensor for quadrotor navigation system, bias compensation, and Zero Velocity Compensation (ZVC). Kalman Filter is designed to reduce the noise on the sensor while bias compensation and ZVC are designed to eliminate the bias and drift error in the sensor data. Test results showed the Kalman Filter design is acceptable to reduce the noise in the sensor data. Moreover, the bias compensation and ZVC can reduce the drift error due to integration process as well as improve the position estimation accuracy of the quadrotor. At the time of testing, the system provided the accuracy above 90 % when it tested indoor.

show abstract