This paper presents an approach for the simultaneous learning and recognition of places applied to autonomous robotics. While noteworthy results have been achieved with respect to off-line training process for appearance-based navigation, novel issues arise when recognition and learning are simultaneous and unsupervised processes. The approach adopted here uses a Gaussian mixture model estimated by a novel incremental MML-EM to model the probability distribution of features extracted by image-preprocessing. A place detector decides which features belong to which place integrating odometric information and a hidden Markov model. Tests demonstrate that the proposed system performs as well as the ones relying on batch off-line environmental learning. ©2007 IEEE.
CITATION STYLE
Chella, A., Macaluso, I., & Riano, L. (2007). Automatic place detection and localization in autonomous robotics. In IEEE International Conference on Intelligent Robots and Systems (pp. 741–746). https://doi.org/10.1109/IROS.2007.4399614
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