Title :
Obstacle detection for low flying UAS using monocular camera
Author :
Zhang, F. ; Goubran, R. ; Straznicky, P.
Author_Institution :
Dept. of Syst. & Comput. Eng., Carleton Univ., Ottawa, ON, Canada
Abstract :
This paper describes an obstacle detection algorithm for low flying unmanned aircraft system (UAS) using an inertial aided inverse depth Extended Kalman Filter (EKF) framework. The EKF framework fuses inertial measurements with monocular image sensor measurements to estimate the positions of a number of landmarks as well as the position and orientation of the UAS. A high resolution sparse terrain elevation map and UAS trajectory can then be computed from the filter state vector. An inverse depth parameterization is used to describe the position of the landmarks so that features at all ranges can be tracked by the filter. A test flight was conducted to test the algorithm in a realistic scenario. The result shows that the algorithm produces accurate terrain elevation model, and is capable of generating accurate high resolution terrain elevation map when image sensor with high resolution and dynamic range is used.
Keywords :
Kalman filters; aircraft; cameras; filters; image sensors; nonlinear filters; EKF framework; filter state vector; high resolution sparse terrain elevation map; inertial aided inverse depth extended Kalman filter framework; inertial measurements; inverse depth parameterization; low flying UAS; low flying unmanned aircraft system; monocular camera; monocular image sensor measurements; obstacle detection algorithm; terrain elevation model; test flight; Cameras; Feature extraction; Geophysical measurements; Global Positioning System; Heuristic algorithms; Image resolution; Vectors; Motion Stereo; Obstacle Detection; Range Estimate; UAS;
Conference_Titel :
Instrumentation and Measurement Technology Conference (I2MTC), 2012 IEEE International
Conference_Location :
Graz
Print_ISBN :
978-1-4577-1773-4
DOI :
10.1109/I2MTC.2012.6229318