3D fast map for autonomous mixed swarm robot drone (MSRD)
This project aims to explore the use of 3D Fast Mapping for efficient mapping of indoor environments using a Jet Racer and a Zed Mini camera. The project objectives include developing a 3D fast map imaging concept, selecting the most appropriate camera and the autonomous device as the carrier o...
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| Format: | Final Year Project |
| Language: | English |
| Published: |
Nanyang Technological University
2023
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| Online Access: | https://hdl.handle.net/10356/166916 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | This project aims to explore the use of 3D Fast Mapping for efficient mapping of indoor
environments using a Jet Racer and a Zed Mini camera. The project objectives include
developing a 3D fast map imaging concept, selecting the most appropriate camera and the
autonomous device as the carrier of the camera while conducting mapping, and finally, to
integrate the components for fast mapping in two indoor environments. The performance of
the 3D Fast Mapping approach was evaluated and compared against conventional mapping in
terms of time taken to complete the map and completeness of the map produced.
Two environments, an indoor corridor and a study room, were mapped using both the
3D Fast Mapping approach and conventional SLAM mapping. The results showed that 3D Fast
Mapping was faster than conventional SLAM mapping in both environments, although the
completeness of the map produced was higher for conventional SLAM mapping. Limitations
of the 3D Fast Mapping approach were identified, including the minimum turning radius of the
Jet Racer and the limited height range of the Zed Mini camera. Recommendations for future
work include using a combination of robots with varying turning radii and height ranges, and
exploring the use of multi-swarm robot drones to overcome these limitations.
Overall, this project demonstrates the potential of 3D Fast Mapping for efficient
mapping of indoor environments, with future work aimed at improving the completeness and
accuracy of the maps produced. The project has practical applications in areas such as
autonomous navigation and robotics, where accurate mapping of indoor environments is
essential for safe and efficient operation |
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