Abstract
A main task for domestic robots is to navigate safely at home, find places and detect objects. We set out to exploit the knowledge available to the robot to constrain the task of understanding the structure of its environment, i.e., ground for safe motion and walls for localisation, to simplify object detection and classification. We start from exploiting the known geometry and kinematics of the robot to obtain ground point disparities. This considerably improves robustness in combination with a histogram approach over patches in the disparity image. We then show that stereo data can be used for localisation and eventually for object detection classification and that this system approach improves object detection and classification rates considerably.
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Vincze, M., Wohlkinger, W., Olufs, S., Einramhof, P., Schwarz, R., Varadarajan, K. (2012). Object Detection and Classification for Domestic Robots. In: Hähnle, R., Knoop, J., Margaria, T., Schreiner, D., Steffen, B. (eds) Leveraging Applications of Formal Methods, Verification, and Validation. ISoLA 2011. Communications in Computer and Information Science, vol 336. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34781-8_8
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DOI: https://doi.org/10.1007/978-3-642-34781-8_8
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