Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Land Surveying and Geo-Informatics | en_US |
| dc.contributor.advisor | Shi, Wenzhong (LSGI) | en_US |
| dc.creator | Bao, Sheng | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/12180 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | Multi-sensor integration technologies for backpack mobile mapping systems in cities | en_US |
| dcterms.abstract | A three-dimensional (3-D) point cloud map is a commonly used map type. It is a basis for many fields, such as localization and navigation. A mobile mapping system (MMS) is a widely used moving platform for the generation of 3-D point cloud maps, which requires sensor calibration to guarantee data quality. | en_US |
| dcterms.abstract | Global navigation satellite system (GNSS) sensor and light detection and ranging (LiDAR) are important parts of an MMS, and the extrinsic calibration between them can markedly improve its performance. A sole GNSS sensor is generally used in a portable MMS, which is the current trend. However, existing extrinsic calibration methods of GNSS/inertial navigation system (INS) are not applicable for it. It can only provide three degrees of freedom (DoF) positions, while these methods require six DoF pose outputs for both sensors. Here, two sole GNSS sensor extrinsic calibration methods are proposed in the thesis: one is a target-based direct calculation method and the other is a non-target-based automatic iterative method. Besides, calibration between IMU and LIDAR is also implemented. | en_US |
| dcterms.abstract | GNSS positioning information is helpful to improve mapping performance, but it is bad and not available in many environments. Methods that can be used under these conditions are studied in the thesis. This thesis proposes a tight coupling mapping method that integrates the error-state Kalman filter (ESKF), the general framework for graph optimization (g2o), and the point cloud alignment. An ESKF is used to provide initial estimations for point cloud alignment and fuse the output pose of the g2o. A g2o is used to optimize the point cloud alignments of frame to frame and frame to the local map. They are both used to improve mapping performance in non-monotonous environments. | en_US |
| dcterms.abstract | In the monotonous environment, current mapping methods have inadequate performance because of lacking geometric features and constraints for point cloud alignment. A systematic pedestrian dead reckoning (PDR) augmentation mapping framework for backpack MMS is proposed to solve the challenging problem. The framework starts with data acquisition with LiDARs and an inertial measurement unit (IMU), followed by the proposed lightweight monotonous scene recognition method based on statistical features. Then, a step detection based on four-layer long short-term memory (LSTM) networks is implemented, and a PDR module is used to provide extra positioning constraints. Lastly, the PDR information is fused with the LiDAR odometry by a factor graph (FG), and a 3-D point cloud map is built based on the output of the FG. | en_US |
| dcterms.abstract | Finally, experiments are conducted to verify the proposed methods. The experiment results show the extrinsic calibration methods have good accuracy and repeatability. For the tight coupling mapping method, the results indicate that the generated point cloud map can reach the centimeter-level. Additionally, the systematic PDR augmentation mapping framework is evaluated in two common monotonous environments, where GNSS signals are weak. The final mapping accuracy of the system can be improved from meter-level to decimeter-lever in a tunnel, and to centimeter-level in an alley, validating its feasibility. | en_US |
| dcterms.extent | xx, 100 pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2022 | en_US |
| dcterms.educationalLevel | Ph.D. | en_US |
| dcterms.educationalLevel | All Doctorate | en_US |
| dcterms.LCSH | Digital mapping | en_US |
| dcterms.LCSH | Mobile computing | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | open access | en_US |
Copyright Undertaking
As a bona fide Library user, I declare that:
- I will abide by the rules and legal ordinances governing copyright regarding the use of the Database.
- I will use the Database for the purpose of my research or private study only and not for circulation or further reproduction or any other purpose.
- I agree to indemnify and hold the University harmless from and against any loss, damage, cost, liability or expenses arising from copyright infringement or unauthorized usage.
By downloading any item(s) listed above, you acknowledge that you have read and understood the copyright undertaking as stated above, and agree to be bound by all of its terms.
Please use this identifier to cite or link to this item:
https://theses.lib.polyu.edu.hk/handle/200/12180