Global Self-localization by Recognizing Spatial Categories and Guide Signs based on Semantic Segmentation

Abstract

This paper proposes a method of global self-localization based on a deep neural network of spatial feature recognition. The spatial feature recognition network consists of four modules of a spatial feature extraction CNN,a spatial category classification CNN,a semantic segmentation network for estimating surrounding semantic segment distribution and an instance category classification CNN.Global self-localization is performed based on instance categories and guide signs which is recognized by OCR of sign segments. Experiments are conducted for evaluating performance of the proposed global localization method.