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Enhanced PCA-Based Localization Using Depth Maps with Missing Data
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DOI：10.1007/s10846-013-0013-6.png)
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Abstract
En 中文
In this paper a new method for self-localization of mobile robots, based on a PCA positioning sensor to operate in unstructured environments, is proposed and experimentally validated. The proposed PCA extension is able to perform the eigenvectors computation from a set of signals corrupted by missing data. The sensor package considered in this work contains a 2D depth sensor pointed upwards to the ceiling, providing depth images with missing data. The positioning sensor obtained is then integrated in a Linear Parameter Varying mobile robot model to obtain a self-localization system, based on linear Kalman filters, with globally stable position error estimates. A study consisting in adding synthetic random corrupted data to the captured depth images revealed that this extended PCA technique is able to reconstruct the signals, with improved accuracy. The self-localization system obtained is assessed in unstructured environments and the methodologies are validated even in the case of varying illumination conditions.
Keywords:
Mobile robots
Robot sensing systems
Sensor fusion
Principal component analysis
Kalman filters
Journal
J
IF:
2.8
Papers:
3.8K
Citations:
6.9K