NDLI: Multi-Scale Saliency-Guided Compressive Sensing Approach to Efficient Robotic Laser Range Measurements

Content Provider	IEEE Xplore Digital Library
Author	Schwartz, S. Wong, A. Clausi, D.A.
Copyright Year	2012
Abstract	Improving laser range data acquisition speed is important for many robotic applications such as mapping and localization. One approach to reducing acquisition time is to acquire laser range data through a dynamically small subset of measurement locations. The reconstruction can then be performed based on the concept of compressed sensing (CS), where a sparse signal representation allows for signal reconstruction at sub-Nyquist measurements. Motivated by this, a novel multi-scale saliency-guided CS-based algorithm is proposed for an efficient robotic laser range data acquisition for robotic vision. The proposed system samples the objects of interest through an optimized probability density function derived based on multi-scale saliency rather than the uniform random distribution used in traditional CS systems. Experimental results with laser range data from indoor and outdoor environments show that the proposed approach requires less than half the samples needed by existing CS-based approaches while maintaining the same reconstruction performance. In addition, the proposed method offers significant improvement in reconstruction SNR compared to current CS-based approaches.
Starting Page	1
Ending Page	8
File Size	692787
Page Count	8
File Format	PDF
ISBN	9781467312714
DOI	10.1109/CRV.2012.8
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-05-28
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image coding Measurement by laser beam compressed sampling and compressive sensing robotic vision Robot sensing systems range data acquisition Noise measurement Image reconstruction laser range measurement Signal to noise ratio
Content Type	Text
Resource Type	Article

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