Behavior coordination in structured environments

Althaus, Philipp; Christensen, Henrik I.

doi:10.1163/156855303769157009

Cited by 45 publications

(21 citation statements)

References 13 publications

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“…Koenig and Simmons (1998) used a pre-programmed routine to detect doorways from range data. In addition, Althaus and Christensen (2003) used line features to detect corridors and doorways. In their work, Buschka and Saffiotti (2002) partitioned grid maps of indoor environments into two different classes of open spaces, i.e.…”

Section: Related Workmentioning

confidence: 99%

Multi-modal Semantic Place Classification

Pronobis

Mozos

Caputo

et al. 2009

The International Journal of Robotics Research

140

106

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Section: Related Workmentioning

confidence: 99%

Multi-modal Semantic Place Classification

Pronobis

Mozos

Caputo

et al. 2009

The International Journal of Robotics Research

140

106

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show abstract

“…For example, Althaus et al [8] use line features like detected edges to determine corridors and doorways. In Ohya et al [6], an expectation image is first rendered from training images as a model, and then the current location of the robot is determined by matching edges extracted from the model image and a query image, respectively.…”

Section: A Previous Work In General Mobile Robot Localizationmentioning

confidence: 99%

Effective venue image retrieval using robust feature extraction and model constrained matching for mobile robot localization

Feng

Ren

Jiang

et al. 2011

Machine Vision and Applications

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“…Koenig and Simmons [9] apply a pre-programmed routine to detect doorways. Finally, Althaus and Christensen [1] use sonar data to detect corridors and doorways. Learning algorithms have additionally been used to identify objects in the environment.…”

Section: Related Workmentioning

confidence: 99%

Supervised semantic labeling of places using information extracted from sensor data

Mozos

Triebel

Jensfelt

et al. 2007

Robotics and Autonomous Systems

148

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Abstract-Indoor environments can typically be divided into places with different functionalities like corridors, kitchens, offices, or seminar rooms. The ability to learn such semantic categories from sensor data enables a mobile robot to extend the representation of the environment facilitating the interaction with humans. As an example, natural language terms like "corridor" or "room" can be used to communicate the position of the robot in a map in a more intuitive way. In this work, we first propose an approach based on supervised learning to classify the pose of a mobile robot into semantic classes. Our method uses AdaBoost to boost simple features extracted from range data and vision into a strong classifier. We present two main applications of this approach. Firstly, we show how our approach can be utilized by a moving robot for an online classification of the poses traversed along its path using a hidden Markov model. Secondly, we introduce an approach to learn topological maps from geometric maps by applying our semantic classification procedure in combination with a probabilistic relaxation procedure. We finally show how to apply associative Markov networks (AMNs) together with AdaBoost for classifying complete geometric maps. Experimental results obtained in simulation and with real robots demonstrate the effectiveness of our approach in various indoor environments.

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Behavior coordination in structured environments

Cited by 45 publications

References 13 publications

Multi-modal Semantic Place Classification

Multi-modal Semantic Place Classification

Effective venue image retrieval using robust feature extraction and model constrained matching for mobile robot localization

Supervised semantic labeling of places using information extracted from sensor data

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