Repository regrouping scholar project
The aim of this coursework is to fuse data from a variety of sensors to obtain an optimal position, velocity and heading solution.
This coursework uses an optimized version of GTSAM called iSAM (GTSAM is a library of C++ classes that implement smoothing and mapping (SAM) in robotics and vision, using factor graphs and Bayes networks as the underlying computing paradigm rather than sparse matrices.) This project aims at investigate and develop the main components of a vehicle-based SLAM system. The tracking part is based on a non-linear Kalman Filter and integrate data from three types of sensors: a vehicle odometry sensor (relative transformations), GPS (modelled as direct measurement of position) and a range-bearing sensor which measures range to the landmarks.