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[Dissertation.pdf](https://jyjblrd.github.io/part_II_project_dissertation/Dissertation/dissertation.pdf)
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In this project, I:
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1. Design and implement a novel distributed monocular visual SLAM system, capable of
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localization, relative pose estimation, and collaborative mapping, all while being tolerant
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to degraded network conditions and not reliant on any single leader agent (section 3.2).
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2. Evaluate the performance of my system on standard datasets, **demonstrating its su-
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perior performance over comparable state-of-the-art systems (section 4.3).**
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3. Create a simulation environment for testing and evaluating my system locally (sec-
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tion 3.6).
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4. Develop a custom collision avoidance framework (section 3.3) and deploy it alongside
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my SLAM system on physical robots, **demonstrating the practical use cases of my
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system and benchmarking real-world performance (section 4.4).**
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5. **Contribute as a co-author to the paper The Cambridge RoboMaster: An Agile
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Multi-Robot Research Platform**. My distributed SLAM system is included in
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the paper and used to evaluate the robotics platform (section 3.7.1).
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6. Develop Multi-Agent EVO – the first open-source evaluation library for multi-agent SLAM
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systems (section 3.5).
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7. Develop the Raspberry Pi Video Publisher – a performant platform for SLAM data collec-
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tion and augmented reality visualizations – and set up a continuous integration and de-
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ployment pipeline to automatically deploy the latest builds to the devices (section 3.7.3).
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https://github.com/jyjblrd/distributed_visual_SLAM/assets/40762456/6db2d506-c0fd-4976-94fb-ae1da50cfd12
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