Vanishing point detection in corridor for autonomous mobile robots using monocular low-resolution fisheye vision

It is crucial for mobile robots to implement vanishing point detection during navigation in corridors. For the fisheye vision, the conventional methods of vanishing point detection usually obtain poor detection results. This is mainly attributed to serious barrel distortion in images acquired from fisheye cameras that are widely used in mobile robot systems. In the proposed system, a novel vanishing point detection algorithm based on the Gabor filter bank and the convolutional neural network is put forward to realize more accurate detection. The Gabor filter bank is used to extract image texture information in the preprocessing step, thereby enhancing the generalization. The convolutional neural network is used to predict the position of the vanishing point in the fisheye images. To improve the real-time performance and guarantee the accuracy, the low-resolution image should be selected as the input image as far as possible. For this purpose, a multi-resolution experiment was carried out. With the appropriate resolution, the proposed vanishing point detector was found still effective even if 60% of the original information was discarded. In addition, an experiment was conducted to verify the generalization on the condition of illumination changing, pedestrians passing, and different corridor appearance. The experiments displayed good effect and generalization on fisheye images captured in the corridor.