Papers
2
Total Citations
18
H-Index
2
About
Sanming Song is a leading researcher in underwater robotics and sonar perception, with a focus on advancing autonomous navigation and environmental mapping in challenging subsea environments. His major contributions lie in developing novel methods for sonar data processing, particularly for mechanical scanning imaging sonar (MSIS) and forward-looking sonar systems. Song’s work on scan registration using symmetrical Kullback–Leibler divergence (cited 13 times) addresses the critical challenge of coarse spatial and temporal resolution in MSIS, enabling more reliable underwater robot localization and mapping. He also pioneered seabed terrain 3D reconstruction from 2D forward-looking sonar, as demonstrated in a sea-trial report from a pipeline burying project (5 citations), where his techniques proved effective even in turbid, stirred conditions that degrade optical sensors. These achievements have direct applications in portable and economic underwater robots for inspection, construction, and environmental monitoring. Song’s research bridges the gap between theoretical sonar signal processing and real-world marine operations, making him a notable figure in field robotics and underwater perception.
Research Focus
Key Achievements
Top Papers
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