Adaptive visual obstacle detection for mobile robots using monocular camera and ultrasonic sensor
Tek, Faik Boray
İyidir, İbrahim Kamil
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This paper presents a novel vision based obstacle detection algorithm that is adapted from a powerful background subtraction algorithm: ViBe (VIsual Background Extractor). We describe an adaptive obstacle detection method using monocular color vision and an ultrasonic distance sensor. Our approach assumes an obstacle free region in front of the robot in the initial frame. However, the method dynamically adapts to its environment in the succeeding frames. The adaptation is performed using a model update rule based on using ultrasonic distance sensor reading. Our detailed experiments validate the proposed concept and ultrasonic sensor based model update.