Research on Navigation and Positioning of Electric Inspection Robot Based on Improved CNN Algorithm

Abstract

In order to improve the visual navigation performance in complex environment, a robust visual navigation method for substation inspection robot is proposed in this paper. Based on the robustness of hexagonal cone model to light changes, this method can solve the squeezing problem of navigation path in complex environment and reduce the interference caused by external light factors. Based on HM preprocessed images, semantic segmentation is carried out with deep convolutional neural network to obtain global features, local features, and multiscale information of images, so as to effectively improve the network recognition accuracy. The results show that the images after HM color space transformation and grayscale reconstruction can compress the color space while preserving the edge details, which is beneficial to the semantic segmentation network for further scene road recognition. Because the original structure of the network is not adjusted and the corresponding preprocessing layer is added, the size of the network model is relatively increased, but the reasoning speed of the original network is significantly improved, which is 16.4% on average.