Mobile robot localization using an incremental eigenspace model

Matej Artač and Matjaž Jogan and Aleš Leonardis (2002) Mobile robot localization using an incremental eigenspace model. In: 2002 IEEE International Conference on Robotics and Automation, May 11-15, 2002, Washington, D. C..

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Abstract

When using appearance-based recognition for self-localization of mobile robots, the images obtained during the exploration of the environment need to be efficiently stored in the memory. PCA offers means for representing the images in a low-dimensional subspace, which allows for efficient matching and recognition. For active exploration it is necessary to use an incremental method for the computation of the subspace. While such methods have been considered before, only the on-line construction of eigenvectors has been addressed. Representations of the images in the subspace were computed only after the final subspace had been built, requiring that all the images were kept in the memory. In this paper we propose to use an incremental PCA algorithm with the updating of partial image representations in a way that allows the robot to discard the acquired images immediately after the update. Such a model is open-ended, meaning that we can easily update it with new images. We show that the performance of the proposed method is comparable to the performance of the batch method in terms of compression, computational cost and the precision of localization. We also show that by applying the repetitive learning, the subspace converges to that constructed with the batch method.

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