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| Peter Whaite, Frank P. Ferrie, "Autonomous Exploration: Driven by Uncertainty," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 3, pp. 193-205, March, 1997. | |||
| BibTex | x | ||
| @article{ 10.1109/34.584097, author = {Peter Whaite and Frank P. Ferrie}, title = {Autonomous Exploration: Driven by Uncertainty}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {19}, number = {3}, issn = {0162-8828}, year = {1997}, pages = {193-205}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.584097}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - JOUR JO - IEEE Transactions on Pattern Analysis and Machine Intelligence TI - Autonomous Exploration: Driven by Uncertainty IS - 3 SN - 0162-8828 SP193 EP205 EPD - 193-205 A1 - Peter Whaite, A1 - Frank P. Ferrie, PY - 1997 KW - Autonomous exploration KW - active vision KW - visual servoing KW - artificial perception KW - unstructured environments KW - volumetric models KW - superellipsoids KW - next best view KW - theory of optimal experiments. VL - 19 JA - IEEE Transactions on Pattern Analysis and Machine Intelligence ER - | |||
Abstract—Passively accepting measurements of the world is not enough, as the data we obtain is always incomplete, and the inferences made from it uncertain to a degree which is often unacceptable. If we are to build machines that operate autonomously, they will always be faced with this dilemma, and can only be successful if they play a much more active role. This paper presents such a machine. It deliberately seeks out those parts of the world which maximize the fidelity of its internal representations, and keeps searching until those representations are acceptable. We call this paradigm
This paper has two major contributions. The first is a theory that tells us how to explore, and which confirms the intuitive ideas we have put forward previously. The second is an implementation of that theory. In our laboratory, we have constructed a working autonomous explorer and here, for the first time, show it in action. The system is entirely bottom-up and does not depend on any a priori knowledge of the environment. To our knowledge, it is the first to have successfully closed the loop between gaze planning and the inference of complex 3D models.
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