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Proceedings Paper

Real-time optical flow estimation on a GPU for a skied-steered mobile robot
Author(s): V. V. Kniaz
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Paper Abstract

Accurate egomotion estimation is required for mobile robot navigation. Often the egomotion is estimated using optical flow algorithms. For an accurate estimation of optical flow most of modern algorithms require high memory resources and processor speed. However simple single-board computers that control the motion of the robot usually do not provide such resources. On the other hand, most of modern single-board computers are equipped with an embedded GPU that could be used in parallel with a CPU to improve the performance of the optical flow estimation algorithm. This paper presents a new Z-flow algorithm for efficient computation of an optical flow using an embedded GPU. The algorithm is based on the phase correlation optical flow estimation and provide a real-time performance on a low cost embedded GPU. The layered optical flow model is used. Layer segmentation is performed using graph-cut algorithm with a time derivative based energy function. Such approach makes the algorithm both fast and robust in low light and low texture conditions. The algorithm implementation for a Raspberry Pi Model B computer is discussed. For evaluation of the algorithm the computer was mounted on a Hercules mobile skied-steered robot equipped with a monocular camera. The evaluation was performed using a hardware-in-the-loop simulation and experiments with Hercules mobile robot. Also the algorithm was evaluated using KITTY Optical Flow 2015 dataset. The resulting endpoint error of the optical flow calculated with the developed algorithm was low enough for navigation of the robot along the desired trajectory.

Paper Details

Date Published: 29 April 2016
PDF: 12 pages
Proc. SPIE 9897, Real-Time Image and Video Processing 2016, 989706 (29 April 2016); doi: 10.1117/12.2227556
Show Author Affiliations
V. V. Kniaz, GosNIIAS (Russian Federation)

Published in SPIE Proceedings Vol. 9897:
Real-Time Image and Video Processing 2016
Nasser Kehtarnavaz; Matthias F. Carlsohn, Editor(s)

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