Monocular vision based obstacle detection

Document Type : Original Article


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


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%.


Al-Kaff, A., García, F., Martín, D., De La Escalera, A., & Armingol, J. M. (2017). Obstacle Detection and Avoidance System Based on Monocoular camera and Size Expansion Algorithm for UAVs. Sensors, 17(5),1-22.
Ariyur, K., Lommel, P., & Enns, D. (2005). Reactive inflight obstacle avoidance via radar feedback. in American Control Conference, Proceedings of 2005, 4, 2978–2982.
Heidarsson, H.K., & Sukhatme, G.S. (2011). Obstacle detection and avoidance of an autonomous surface vehicle using a profiling sonar. In: Proceedings of 2011 IEEE International Conference on Robotics and Automation, 731–736.
Huh, S., Sungwook, C., Yeondeuk, J., & David, S. (2015). Vision-Based Sense-and Avoid Framework for Unmanned Aerial Vehicles. IEEE Transactions On Aerospace And Electronic Systems, 51(4), 3427-3439.
Labayrade, R., Aubert, D., & Tarel, J.P. (2002). Real-time obstacle detection in stereo vision on non-flat road geometry through “v-disparity” representation. In: Proceedings of the 2002 IEEE Intelligent Vehicles Symposium, 2, 646–651.
Mashaly, A., Yunhong, W., & Qingjie, L. (2016).  Efficient sky segmentation approach for small UAV autonomous obstacles avoidance in cluttered environment. 2016 IEEE International Geoscience and Remote Sensing Symposium.
Menezes, P., Dias, J., Ara´ujo, H., & de Almeida, A. (2005). Low cost sensor based obstacle detection and description. Lecture Notes in Control and Information Sciences, 223, 231–237.
Mori, T., & Scherer, S. (2013). First results in detecting and avoiding frontal obstacles from a monocular camera for micro unmanned aerial vehicles. IEEE International Conference on Robotics and Automation (ICRA), Germany, 1750-1757.
 Park, J., & Youdan, K. (2015). Collision avoidance for Quadrotor using stereo vision. IEEE Transactions on Aerospace and Electronic Systems, 51(4), 3226-3241.
Shang, E., An, X., Li, J., & He, H. (2014). A novel setup method of 3d LIDAR for negative obstacle detection in field environment. in 2014 IEEE17th International Conference on Intelligent Transportation Systems (ITSC), 1436–1441.
Shim, D., Chung, H., & Sastry, S. (2006).Conflict-free navigation in unknown urban environments. IEEE Robotics Automation Magazine, 13(3), 27–33.
Zeng, Y., Feifei, Z., Guiaiang, W., Lingyu, Z., & Bo, X. (2016). Brain-Inspired Obstacle Detection Based on the Biological Visual Pathway. Brain Informatics and Health, 9919, 355-364.