Antoni Burguera Burguera | Systems, Robotics & Vision Group

Hierarchical Color Encoding for Progressive Image Transmission in Underwater Environments

This letter presents the Progressive Hierarchical Image Encoding (PHIE) approach aimed at progressively encoding underwater images and, thus, helping in transmitting them in unreliable, low bandwidth, subaquatic communication channels. The proposal splits an image...

Progressive Hierarchical Encoding for Image Transmission in Underwater Environments

This paper proposes a method to progressively encode underwater images. The image information is split into several, very small, parts called chunks that can be easily transmitted using unreliable, low bandwidth, underwater communication channels. The receiver can...

A Loop Selection Front-end for Underwater Visual GraphSLAM

This paper presents an algorithm to reject false loops ready to be used as a front-end for GraphSLAM. The proposal operates in two steps. The first one checks each loop independently to reduce the computational cost of the second one, which jointly checks the...

Robust Underwater Visual Graph SLAM using a Siamese Neural Network and Robust Image Matching

This paper proposes a fast method to robustly perform Visual Graph SLAM in underwater environments. Since Graph SLAM is not resilient to wrong loop detections, the key of our proposal is the Visual Loop Detector, which operates in two steps. First, a lightweight...

Lightweight Underwater Visual Loop Detection and Classification using a Siamese Convolutional Neural Network

This paper presents an end-to-end Neural Network (NN) to estimate the overlap between two scenes observed by an underwater robot endowed with a bottom-looking camera. This information is extremely valuable to perform visual loop detection in Simultaneous Localization...

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