## 1 Introduction  
Conventional approaches to robot navigation mainly rely on metric maps to provide rich geometric information of the environment. Metric maps are easy to maintain and can provide precise navigation strategies for the mobile robot [1; 2]. However, this kind of map will cause the problem of large-scale structural ambiguity and lack semantic information of the environment, which is not conducive to human-robot interaction [3; 4].  
In recent years, computer vision has been extensively applied to robot navigation [5; 6; 7]. Since it can provide rich features of environment which is conductive to eliminate the ambiguity of objects and places categories compared with traditional range sensors. As a highly abstracted graph-based representation of the environment, the vision-based topological map does not require precise location information of the robot and compactly describes the environment as topological diagram with nodes and edges, in which each node corresponds to a scenario with distinctive features and each edge encodes adjacency relationships between nodes [8; 9]. Global consistent information is not essential when the robot navigates in a local region. Just as a person perceives a novel environment, he is unlikely to take notice of all the details of the environment, but only record the typical local landmarksthat is conductive to navigate even in a complex environment, such as corners and doors. Compared to a metric map, the topological map takes up less storage memory and, consequently, is suitable for long distance qualitative navigation [10]. More specifically, vision-based topological mapping has drawn extensive attention due to its low cost and rich informative representation of the environment. In vision-based topological mapping, local feature descriptors are generally adopted to search visual keyframes for estimating relative pose of the robot, such as binary robust independent element feature (BRIEF) [11] and scale-invariant feature transform (SIFT) [12]. In practice, such feature descriptions perform well for providing certain degree of invariance in camera rotations and image aliasing, but they are short of ability to cope well with some challenging visual scenarios in indoor navigation, such as large illumination variance and lack of texture, because these methods only extract the local gradient feature description and do not capitalize on other discriminative global information encoded in images. In a nutshell, the improvement of robustness and efficiency is still the main issue facing indoor visual navigation [13].  
To tackle above challenges, for this paper, we proposed a scalable vision-based topological mapping and navigation method for a mobile robot to work robustly and flexibly in large-scale indoor environment, exploiting the fine-tuned convolutional neural network (CNN) feature as the image representation to perform image matching. Our major contributions are summarized as follows:  
* An efficient and robust framework with image-based Monte Carlo scheme for indoor topological navigation, namely ERF-IMCS, is presented which combines the image retrieval and Monte Carlo localization to provide the robot with the capability to avoid perceptual aliasing.
* To the best of our knowledge, ERF-IMCS is the first work to employ hash encoding technology to perform efficient CNN feature indexing and visual localization in topological navigation.
* We proposed a 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. Experiments on a real robot platform show that ERF-IMCS exhibits great performance in terms of robustness and efficiency.