A first approach to a Simultaneous Localisation and Mapping (SLAM) solution implementing the Extended Kalman Filter for visual odometry data.
Kalman filter
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Proyecto fin de grado. Electrónica de comunicación.
This project was a done as a _nal grade project to obtain the degree in communications electronics engineering from TECNUN. It was done at Vicomtech under the supervision of Leonardo de Maeztu , Marcos Nieto and Ainhoa Cortes. This project has consisted on the study and a _st approach implementation of a simultaneous localisation and mapping algorithm. The algorithm of choice was the EKF SLAM(Extended Kalman Filter Simultaneous Localisation and Mapping).To properly understand the concepts behind SLAM and its implementation various papers and project reports have been read, but the theoretical implementation and the practical implementation have been mostly based on the report by Jose-Luis Blanco,\Derivation and Implementation of a Full 6D EKF-based Solution to Bearing-Range SLAM"[9]. Also for the implementation , the software C++ has been used with the help of the OpenCV library for the handling and processing of images and matrices. Matlab was also used when complicated math operations needed to be done. The implementations was made with the help of previous code provided by Vicomtech [1] . The code provided contained a successful implementation of a visual odometry problem and it included most of the image processing needed for the next steps of this project.

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