ERF-IMCS: An efficient and robust framework with image-based monte carlo scheme for indoor topological navigation

Conventional approaches to global localization and navigation mainly rely on metric maps to provide precise geometric coordinates, which may cause the problem of large-scale structural ambiguity and lack semantic information of the environment. This paper presents a scalable vision-based topological mapping and navigation method for a mobile robot to work robustly and flexibly in large-scale environment. In the vision-based topological navigation, an image-based Monte Carlo localization method is presented to realize global topological localization based on image retrieval, in which fine-tuned local region features from an object detection convolutional neural network (CNN) are adopted to perform image matching. The combination of image retrieval and Monte Carlo provide the robot with the ability to effectively avoid perceptual aliasing. Additionally, we propose an effective visual localization method, simultaneously employing the global and local CNN features of images to construct discriminative representation for environment, which makes the navigation system more robust to the interference of occlusion, translation, and illumination. Extensive experimental results demonstrate that ERF-IMCS exhibits great performance in the robustness and efficiency of navigation.