Monocular vision based obstacle detection

Document Type : Original Article

Authors

Faculty of Surveying and Geomatics Engineering, K.N. Toosi University of Technology, Tehran, Iran

Abstract

Detecting and preventing incidents with obstacles is a challenging problem. Most of the common obstacle
detection techniques are currently sensor-based. Mobile robots like Small Unmanned Aerial Vehicles
(UAVs) are not able to carry obstacle detection sensors such as radar; therefore, vision-based methods are
considered, which can be divided into stereo and mono techniques. Mono methods are classified into two
groups: Foreground-background separation, and brain-inspired methods. Brain-inspired methods are
highly efficient in obstacle detection. A recent research in this field has focused on matching the ScaleInvariant Feature Transform (SIFT) points along with SIFT size-ratio factor and area-ratio of convex hulls
in two consecutive frames to detect obstacles. However, this method is not able to distinguish between
near and far obstacles nor the obstacles in a complex environment and, thus, is sensitive to wrong matched
points. This paper aims to solve the aforementioned problems through using the distance-ratio of matched
points. Then, every point is investigated for distinguishing between far and near obstacles. The results
demonstrated the high efficiency of the proposed method in complex environments. The least achieved
accuracy of the algorithm was 60.0%, and the overall accuracy was 79.0%.

Keywords

References

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Earth Observation and Geomatics Engineering
Volume 1, Issue 2 - Serial Number 2
December 2017
Pages 122-130

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