Design and analysis of different semantic SLAM algorithms

Abstract: The goal of this thesis project was to improve trajectory estimation of a traditional SLAM framework using semantic information generated by a deep neural network. The first part of the project involved designing and implementing a semantic integration method, in order to semantically classify keypoints and 3D map points within the pipeline. In the second part of the project, multiple bundle adjustment modifications were designed and implemented. Finally, the different methods were evaluated on a widely used SLAM benchmark. The final, proposed method outperforms the baseline on most of the benchmark sequences.