Mobile robot navigation in indoor environments using object and character recognition

Tomono, Masahiro; Yuta, Shin’ichi

doi:10.1109/robot.2000.844076

Cited by 32 publications

(12 citation statements)

References 8 publications

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“…Current semantic scene recognition mainly relies on extension of pattern classification algorithms to robots' sensors. M. Tomono and S. Yuta concentrated on specific robot tasks and used character recognition to get room number and object recognition to detect doors [17]. Francesco Orabona and Claudio Castellini introduced online independent support vector machines to reduce the size of the learning machine while keeping its accuracy [18].…”

Section: Related Workmentioning

confidence: 99%

Indoor scene recognition via probabilistic semantic map

Meng

2012

2012 IEEE International Conference on Automation and Logistics

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A domestic robot must recognize its current place accurately and interact with human beings effectively, thus we desire efficient and semantically meaningful scene representation. In this article, we introduce weighted component pooling to analyze indoor scenes, and probabilistic semantic mapping to represent them based on interactive robot learning. We test this algorithm with 10 scene types from an indoor scene recognition image set and 5 scene types with a humanoid robot in domestic settings. Our result shows that the robot can learn and find desired place according to our verbal commands accurately.

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Section: Related Workmentioning

confidence: 99%

Indoor scene recognition via probabilistic semantic map

Meng

2012

2012 IEEE International Conference on Automation and Logistics

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“…(They suggest it may be useful for robotic navigation but do not demonstrate such an application which would have required the navigation, mapping, and text-recognition components that are integrated into our system.) In [16], an algorithm is presented to detect a set of known (pre-specified) landmarks where some include text such as room numbers, and the authors of [17] provide a model of their environment's hallways and doors so that the robot can search for a given room by first matching the door model and then using it to locate the target text. In contrast to these last two, we provide no prior knowledge of what rooms are present nor any model of the environment beyond very basic assumptions about the general locations of signs.…”

Section: Related Workmentioning

confidence: 99%

Autonomous sign reading for semantic mapping

Case

Suresh

Coates

et al. 2011

2011 IEEE International Conference on Robotics and Automation

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Abstract-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 SLAMgenerated 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.

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“…Furthermore, if the landmark found is an office's nameplate, the next step is reading its contents. This ability is widely used by humans, and other research approaches have been done recently in this sense (Tomono, Yuta, 2000). In our work, a simple Optical Character Recognition (OCR) algorithm has been designed for the reading task.…”

Section: Extraction Of Landmark Associated Informationmentioning

confidence: 99%

Learning Visual Landmarks for Mobile Robot Navigation

Mata

Armingol

Escalera

et al. 2002

IFAC Proceedings Volumes

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This paper describes a vision-based landmark learning and recognition system for use in mobile robot navigation tasks. The system uses genetic algorithms (GA) for both learning and recognition processes. It is able to learn new landmarks with very little human intervention. The recognition system can read text inside landmarks, when present. This learning ability has been tested with two very different landmarks that have been successfully used for indoor topological robot navigation. In addition, some new landmarks are learnt, that will be tested for indoor-outdoor navigation in future works. The presented experimental results show the performances of the proposed algorithm.

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Mobile robot navigation in indoor environments using object and character recognition

Cited by 32 publications

References 8 publications

Indoor scene recognition via probabilistic semantic map

Indoor scene recognition via probabilistic semantic map

Autonomous sign reading for semantic mapping

Learning Visual Landmarks for Mobile Robot Navigation

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