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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書目詳細資料
主要作者: Ooi, Jia Min
其他作者: Li King Ho Holden
格式: Final Year Project
語言:English
出版: Nanyang Technological University 2023
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在線閱讀:https://hdl.handle.net/10356/166916
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機構: Nanyang Technological University
語言: English
實物特徵
總結: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