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We propose a robust and efficient method to estimate the pose of a camera with respect to complex 3D textured models of the environment that can potentially contain more than 100; 000 points. To tackle this problem we follow a top down approach where we combine high-level deep network classifiers with low level geometric approaches to come up with a solution that is fast, robust and accurate. Given...
The problem acoustic localization for autonomous underwater vehicles (AUVs) is discussed in the framework of kernel-based models. The approach consists of improving the measurement of time of fligth of acoustic signals with the help of the Auto-Associative Kernel Regression (AAKR). This makes localization less sensitive to perturbations in the acoustic channel improving the estimation filter convergence...
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