Development of an intelligent state machine for drone navigating from outdoor to indoor
This report presents an intelligent state machine that enables the drone to seamlessly navigate from outdoor to indoor. Traditionally, the Global Position System (GPS) is commonly used in outdoor navigation but has a limitation in indoor or urban environments. Light Detection and Ranging (LiDA...
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| 其他作者: | |
| 格式: | Final Year Project |
| 語言: | English |
| 出版: |
Nanyang Technological University
2024
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/176447 |
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| 總結: | This report presents an intelligent state machine that enables the drone to seamlessly
navigate from outdoor to indoor. Traditionally, the Global Position System (GPS) is
commonly used in outdoor navigation but has a limitation in indoor or urban
environments. Light Detection and Ranging (LiDAR) is widely used in indoor or urban
navigation. In this project, GPS and LiDAR sensors are utilised in order for the drone
to navigate smoothly from outdoor to indoor. The project focuses on developing a
system to provide a pose estimate and map by fusing the Light Detection and Ranging
(LiDAR), Inertial Measurement Unit (IMU) and Global Position System (GPS) sensor
data. The proposed system utilised the Iterated Extended Kalman Filter (IEKF) to
provide LiDAR odometry and keyframes by fusing the LiDAR and IMU
measurements. The pose estimate and map are optimised by the pose graph
optimisation method which uses a factor graph and incremental smoothing and
mapping (iSAM2) algorithm. The proposed system is also able to perform loop
detection using the Scan Context approach to correct the drifting effect. Finally, an
optimised robot’s pose and map are provided by the proposed system which is able to
be used for seamless outdoor to indoor transition. |
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