Obstacle Detection Using Monocular Camera for Low Flying Unmanned Aerial Vehicle

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Zhang, Fan




This thesis describes the research of an obstacle detection system for
a low flying autonomous unmanned aerial vehicle(UAV). The system
utilized an extended Kalman filter based simultaneous localization and
mapping algorithm which fuses navigation measurements with monocular
image sequence to estimate the poses of the UAV and the positions of

To test the algorithm with real aerial data, a test flight was
conducted to collect data by using a sensors loaded simulated unmanned
aerial system(SUAS) towed by a helicopter. The results showed that the
algorithm is capable of
mapping landmarks ranging more than 1000
meters. Accuracy analysis also showed that SUAS localization and
landmark mapping results generally agreed with the ground truth.

To better understand the strength and weakness of the system, and to
improve future designs, the algorithm was further analyzed through a
series of simulations which simulates oscillatory motion of the UAV,
error embedded in camera calibration result, and quantization error
from image digitization.


Engineering - Aerospace
Artificial Intelligence




Carleton University

Thesis Degree Name: 

Master of Applied Science: 

Thesis Degree Level: 


Thesis Degree Discipline: 

Engineering, Electrical and Computer

Parent Collection: 

Theses and Dissertations

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