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The Application of Mobile Robots for Building Safety Control

Siemiatkowska B., Hrasymowicz-Boggio B., Wisniowski M.

Journal of Automation Mobile Robotics and Intelligent Systems

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2016

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Vol. 10, No. 2

9--14

EN

In this article we propose the application of service mobile robots for control of building safety parameters. Indoor mobile robots are becoming a reality and their availability and applications are expected to grow rapidly in the near future. Such robots are usually equipped with cameras and laser range finders, which could be used to detect hazardous situations in their operating environment, such as evacuation route obstructions, emergency sign occlusions or accumulation of dangerous materials. We demonstrate how these safety-related augmentations of a mobile robot system can be achieved with few additional resources and validate experimentally the concept using an indoor robot for emergency sign and evacuation route control.

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Significance of features in object recognition using depth sensors

Harasymowicz-Boggio B., Chechlinski L, Siemiatkowska B.

Optica Applicata

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2015

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Vol. 45, nr 4

559--571

EN

This article concerns a key topic in the field of visual object recognition – the use of features. Object recognition algorithms typically rely on a fixed vector of pre-selected features extracted from 2D or 3D scenes, which are then analyzed with various classification techniques. On the other hand, the activation of particular features in biological vision systems is hierarchical and data-driven. To achieve a deeper understanding of the subject, we have introduced several mathematical tools to estimate multiple RGB-D features’ relevance for different object recognition tasks and conducted statistical experiments involving our database of high quality 3D point clouds. From the thorough analysis of the obtained results we draw conclusions that may be useful to design better, more adaptive object recognition algorithms.

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Semantic Place Labeling Method

Siemiatkowska B., Harasymowicz-Boggio B., Chechlinski Ł.

Journal of Automation Mobile Robotics and Intelligent Systems

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2015

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Vol. 9, No. 1

28--33

EN

The paper presents a method of semantic localization of a mobile robot. The robot is equipped with a Sick laser finder and a Kinect sensor. The simplest source of informa tion about an environment is a scan obtained by the range sensor. The polygonal approximation of an observed area is performed. The shape of the polygon allows us to distinguish corridors from other places using a simple rule based system. During the next step rooms are classified based on objects which have been recognized. Each object votes for a set of classes of rooms. In a real environment we deal with uncertainty. Usually probabilistic theory is used to solve the problem but it is not capable of capturing subjective uncertainty. In our approach instead of the classic Bayesian method we proposed to perform classification using Dempster-Shafer theory (DST), which can be regarded as a generalization of the Bayesian theory and is able to deal with subjective uncertainty. The experiments performed in real office environment proved the efficiency of our approach.