We consider the problem of automatically collecting semantic labels during robotic mapping by extending the mapping system to include text detection and recognition modules. In particular, we describe a system by which a SLAM-generated map of an office environment can be annotated with text labels such as room numbers and the names of office occupants. These labels are acquired automatically from signs posted on walls throughout a building. Deploying such a system using current text recognition systems, however, is difficult since even state-of-the-art systems have difficulty reading text from non-document images. Despite these difficulties we present a series of additions to the typical mapping pipeline that nevertheless allow us to create highly usable results. In fact, we show how our text detection and recognition system, combined with several other ingredients, allows us to generate an annotated map that enables our robot to recognize named locations specified by a user in 84% of cases.

/pdf/autonomous-sign-reading-for-semantic-mapping-17k8gkkper.pdf

Autonomous sign reading for semantic mapping

This paper describes a vision-based landmark recognition system for use with mobile robot navigation tasks. A search algorithm based on genetic techniques for pattern recognition in digital images is presented. The developed system allows the topologic localization of a mobile robot using natural and artificial landmarks. Text strings inside landmarks can be read and interpreted, if present. The resulting system was tested onboard a B21 mobile robot and proved useful. The presented experimental results show the effectiveness of the proposed algorithm.

http://portal.uc3m.es/portal/page/portal/dpto_ing_sistemas_automatica/home/staff/dr_arturo_de_la_escalera_hueso/publications/icra01.pdf

A visual landmark recognition system for topological navigation of mobile robots

Complex and structured landmarks like objects have many advantages over low-level image features for semantic mapping. Low level features such as image corners suffer from occlusion boundaries, ambiguous data association, imaging artifacts, and viewpoint dependance. Artificial landmarks are an unsatisfactory alternative because they must be placed in the environment solely for the robot's benefit. Human environments contain many objects which can serve as suitable landmarks for robot navigation such as signs, objects, and furniture. Maps based on high level features which are identified by a learned classifier could better inform tasks such as semantic mapping and mobile manipulation. In this paper we present a technique for recognizing door signs using a learned classifier as one example of this approach, and demonstrate their use in a graphical SLAM framework with data association provided by reasoning about the semantic meaning of the sign.

Simultaneous localization and mapping with learned object recognition and semantic data association

Recently, various service robot systems are introduced and offer the guidance and delivery service. However, most of them are operated on a single floor, because they do not consider the elevator environment. In this paper, we present the navigation strategy for multi-floor environment including elevator situation. Firstly, we propose the recognition method of the button and the floor number so that the robot reacts flexibly to a current elevator status. We apply adaptive threshold method to a current image in order to get a binary image and make the candidates of the button and the floor number using additional filtering method. By rejecting weak candidates using the artificial neural network, we finally get the position of button and current floor number. Secondly, we suggest the path planning algorithm for navigating in the elevator. By constructing an occupancy grid map of the inner part of the elevator, we calculate the optimal position to move and make a shortest path using the distance transform.

Navigation strategy for the service robot in the elevator environment

This paper presents practical, high-level topological navigation tasks, making use of our general purpose landmark learning and detection system, which includes the possibility of reading text or icons inside detected landmarks. Room identification from inside, without any initialization, is achieved through its landmark signature. Room search along a corridor is done by reading the content of room nameplates placed around for human use; this allow the robot to take high-level decisions, and results in a higher integration degree of mobile robotics in real life.

Using learned visual landmarks for intelligent topological navigation of mobile robots

Navigation in unknown environments requires the robot to obtain the destination positions without a map. The utilization of model-based object recognition would be a solution, where the robot can estimate the destination positions from geometric relationships between the recognized objects and the robot. This paper presents a robot system for this kind of navigation, in which the robot navigates itself to the room designated by room number. The robot has an environment model including a corridor and a door with a room number plate, and utilizes the model for the efficient recognition of the objects and the estimation of their positions.

S. Yuta

Papers

Mobile robot navigation in indoor environments using object and character recognition