Kernel based surface recons truction in a side-scan sonar simulation framework

In this paper we present an existing framework for surface reconstruction from side-scan data based on a forward simulation stage and an estimation stage which considers vehicular ego-motion. It is discussed how an extension of the method using a kernelized surface representation could look like and discuss approaches from literature like a Bayesian formulation in a Markov Random Field structure.