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7 February 2019 Scan registration for underwater mechanical scanning imaging sonar using symmetrical Kullback–Leibler divergence
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Abstract
Due to its advantages in size and energy consumption, mechanical scanning imaging sonar (MSIS) has been widely used in portable and economic underwater robots to observe the turbid and noisy underwater environment. However, handicapped by the coarseness in spatial and temporal resolution, it is difficult to stitch the scan pieces together into a panoramic map for global understanding. A registration method named symmetrical Kullback–Leibler divergence (SKLD)-distribution-to-distribution (D2D), which models each scan as a Gaussian mixture model (GMM) and evaluates the similarity between two GMMs in a D2D way with the measure defined by SKLD, is proposed to register the scans collected by MSIS. SKLD not only weights the difference between distributions with the prior probability but also increases the numerical stability with the symmetrical constraint in distance measure. Moreover, an approximation strategy is designed to derive a tractable solution for the KLD between two GMMs. Experimental results on the scans that were collected from the realistic underwater environment demonstrate that SKLD-D2D dramatically reduces the computational cost without compromising the estimation precision.
© 2019 SPIE and IS&T 1017-9909/2019/$25.00 © 2019 SPIE and IS&T
Min Jiang, Sanming Song, Fengzhen Tang, Yiping Li, Jian Liu, and Xisheng Feng "Scan registration for underwater mechanical scanning imaging sonar using symmetrical Kullback–Leibler divergence," Journal of Electronic Imaging 28(1), 013026 (7 February 2019). https://doi.org/10.1117/1.JEI.28.1.013026
Received: 9 May 2018; Accepted: 8 January 2019; Published: 7 February 2019
JOURNAL ARTICLE
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