Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Mechanical Engineering | en_US |
| dc.contributor.advisor | Sun, Yuxiang (ME) | en_US |
| dc.creator | Liu, Zhuoyuan | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/12431 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | Development of a localization system for an autonomous vehicle based on IMU and 3D LiDAR | en_US |
| dcterms.abstract | With the large-scale use of mobile robots, the requirements for map construction and localization capabilities are getting higher and higher, and the mapping and localization algorithms of individual sensors are often unable to meet the needs. Therefore, this paper mainly studies LiDAR mapping and localization based on multi-sensor fusion. | en_US |
| dcterms.abstract | Firstly, we derived the hardware system of the unmanned vehicle, including the mechanical design, the sensor configuration and the appearance design of the body. We found a factory to machining the mechanical parts using mechanical drawings. After assembling, to test the sensors as well as the car, we ran open-source systems on the car such as ORBSLAM, gmapping, and movebase. | en_US |
| dcterms.abstract | For multi-sensor fusion mapping and localization, the technique basing on single-LiDAR-SLAM is very important. Thus in this paper we make a study on the different famous map construction technologies (NDT/ICP/LOAM/ALOAM) of a single LiDAR, and improved ICP & LOAM methods by using ceres, a optimization library. With the improved approaches, the efficiency of the whole algorithm is increased comparing to the original ones. Then, we extended the extends the proposed single-laser SLAM algorithm, and implements a multi-sensor fusion mapping algorithm on this basis. Comparing to the single-LiDAR-SLAM, the extended system applied point-cloud-removal algorithm to make a more accurate point cloud, and used keyframes to create maps based on optimization. | en_US |
| dcterms.abstract | Finally, we derived the IMU attitude solution equations, the NDT/IMU fusion localization equations basing on KF, compared the performance on open-source dataset, applied them on the system and achieved accurate localization for the car. | en_US |
| dcterms.extent | 1 volume (unpaged) : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2022 | en_US |
| dcterms.educationalLevel | M.Sc. | en_US |
| dcterms.educationalLevel | All Master | en_US |
| dcterms.LCSH | Remote sensing | en_US |
| dcterms.LCSH | Multisensor data fusion | en_US |
| dcterms.LCSH | Automated vehicles | en_US |
| dcterms.LCSH | Location-based services | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 6879.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 2.96 MB | Adobe PDF | View/Open |
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