This paper describes a Visual SLAM system developed on a mobile robot in order to support localization services to visually impaired people. The proposed system aims to provide services in small or mid-scale environments such as inside a building or campus of school where conventional positioning data such as GPS, WIFI signals are often not available. Toward this end, we adapt and improve existing vision-based techniques in order to handle issues in the indoor environments. We firstly design an image acquisition system to collect visual data. On one hand, a robust visual odometry method is adjusted to precisely create the routes in the environment. On the other hand, we utilize the Fast-Appearance Based Mapping algorithm that is may be the most successful for matching places in large scenarios. In order to better estimate robot’s location, we utilize a Kalman Filter that combines the matching results of current observation and the estimation of robot states based on its kinematic model. The experimental results confirmed that the proposed system is feasible to navigate the visually impaired people in the indoor environments.