國立虎尾科技大學 |

Localization for Autonomous Navigation Systems in Large-Scale Outdoor Environments.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Localization for Autonomous Navigation Systems in Large-Scale Outdoor Environments./
作者:	Yu, Yang.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	116 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-11, Section: B.
Contained By:	Dissertations Abstracts International83-11B.
標題:	Calibration. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29221682
ISBN:	9798438718482

Localization for Autonomous Navigation Systems in Large-Scale Outdoor Environments.
Yu, Yang.

Localization for Autonomous Navigation Systems in Large-Scale Outdoor Environments. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 116 p.

Source: Dissertations Abstracts International, Volume: 83-11, Section: B.

Thesis (Ph.D.)--Hong Kong University of Science and Technology (Hong Kong), 2021.

This item must not be sold to any third party vendors.

Accurate localization is the most fundamental abilities of fully autonomous robots, including unmanned aerial vehicles (UAVs) [90, 46, 22], unmanned ground vehicles (UGVs) [121, 116, 98], and unmanned surface vehicles (USVs) [122, 109, 10]. Although numerous approaches have been adopted to achieve attractive performance on Simultaneous Localization and Mapping (SLAM) tasks in static and indoor scenarios, these tasks in large-scale and appearance-changing environments with robust performance are still challenging. For example, practical applications of mobile robots usually suffer from ineffective observations due to appearance changes, limitations of sensors, insufficient computational resources in large-scale environments, and accumulated drifts after long-term operation. To conquer these challenges, multiple sensors systems with sensor fusion can provide denser, higher frequency, and more dimensional measurements. Cameras, light detection and rangings (LiDARs), inertial measurement units (IMUs), and wheel encoders are common sensors for autonomous systems, especially for UGVs.In this thesis, I propose sensor fusion-based state estimators and localization systems, especially in large-scale outdoor environments. The thesis is divided into visual-based and LiDAR-based chapters. In the visual-based localization chapter, an omnidirectional visual-inertial state estimator is firstly proposed. It adopts panoramic images and inertial measurements to achieve not only robust performance with robot pose estimation but also online calibration of multiple sensors, robot velocity, and sensor bias. Then, I propose a complete visual localization system in the large-scale outdoor port scene. It combines learning-based semantic segmentation results with the prior map to implement robust and high-accuracy localization. I also utilize the proposed visual state estimator to compensate for the wheel odometry. In the multi-LiDAR localization chapter, I start with an automatic multi-LiDAR calibration method. Motion-based and appearance-based calibration methods are utilized for a novel calibration performance without any extra sensors, calibration target, or prior knowledge about surroundings. Based on that, I introduce a LiDAR-based localization approach without a 3D prebuild map, which is also considered to be suitable in challenging port scenes. In this localization approach, I also introduce LiDAR-wheel-encoder odometry with a four-wheel steering model. By conducting a series of experiments both in simulated and real-world large-scale challenging environments, I show proposed approaches with robust and accurate performance in indoor and outdoor mobile robot localization scenarios.

ISBN: 9798438718482Subjects--Topical Terms:

1027585
Calibration.

Localization for Autonomous Navigation Systems in Large-Scale Outdoor Environments.
LDR:03910nam a2200385 4500 001 1067297
005 20220823142341.5
008 221020s2021 ||||||||||||||||| ||eng d
020 $a 9798438718482
035 $a (MiAaPQ)AAI29221682
035 $a (MiAaPQ)HongKongSciTech_991013039228303412
035 $a AAI29221682
040 $a MiAaPQ $c MiAaPQ
100 1 $a Yu, Yang. $3 845414
245 1 0 $a Localization for Autonomous Navigation Systems in Large-Scale Outdoor Environments.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 116 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-11, Section: B.
500 $a Advisor: Liu, Ming.
502 $a Thesis (Ph.D.)--Hong Kong University of Science and Technology (Hong Kong), 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Accurate localization is the most fundamental abilities of fully autonomous robots, including unmanned aerial vehicles (UAVs) [90, 46, 22], unmanned ground vehicles (UGVs) [121, 116, 98], and unmanned surface vehicles (USVs) [122, 109, 10]. Although numerous approaches have been adopted to achieve attractive performance on Simultaneous Localization and Mapping (SLAM) tasks in static and indoor scenarios, these tasks in large-scale and appearance-changing environments with robust performance are still challenging. For example, practical applications of mobile robots usually suffer from ineffective observations due to appearance changes, limitations of sensors, insufficient computational resources in large-scale environments, and accumulated drifts after long-term operation. To conquer these challenges, multiple sensors systems with sensor fusion can provide denser, higher frequency, and more dimensional measurements. Cameras, light detection and rangings (LiDARs), inertial measurement units (IMUs), and wheel encoders are common sensors for autonomous systems, especially for UGVs.In this thesis, I propose sensor fusion-based state estimators and localization systems, especially in large-scale outdoor environments. The thesis is divided into visual-based and LiDAR-based chapters. In the visual-based localization chapter, an omnidirectional visual-inertial state estimator is firstly proposed. It adopts panoramic images and inertial measurements to achieve not only robust performance with robot pose estimation but also online calibration of multiple sensors, robot velocity, and sensor bias. Then, I propose a complete visual localization system in the large-scale outdoor port scene. It combines learning-based semantic segmentation results with the prior map to implement robust and high-accuracy localization. I also utilize the proposed visual state estimator to compensate for the wheel odometry. In the multi-LiDAR localization chapter, I start with an automatic multi-LiDAR calibration method. Motion-based and appearance-based calibration methods are utilized for a novel calibration performance without any extra sensors, calibration target, or prior knowledge about surroundings. Based on that, I introduce a LiDAR-based localization approach without a 3D prebuild map, which is also considered to be suitable in challenging port scenes. In this localization approach, I also introduce LiDAR-wheel-encoder odometry with a four-wheel steering model. By conducting a series of experiments both in simulated and real-world large-scale challenging environments, I show proposed approaches with robust and accurate performance in indoor and outdoor mobile robot localization scenarios.
590 $a School code: 1223.
650 4 $a Calibration. $3 1027585
650 4 $a Optimization. $3 669174
650 4 $a Signal processing. $3 561459
650 4 $a Robots. $3 654842
650 4 $a Unmanned aerial vehicles. $3 1372845
650 4 $a Navigation systems. $3 1372854
650 4 $a Robotics. $3 561941
650 4 $a Vehicles. $3 721652
650 4 $a Construction. $3 1254179
650 4 $a Cameras. $3 812890
650 4 $a Sensors. $3 1003702
650 4 $a Maps. $3 889832
650 4 $a Mapping. $3 1215615
650 4 $a Outdoors. $3 1372855
650 4 $a Algorithms. $3 527865
650 4 $a Semantics. $3 555362
650 4 $a Clouds. $3 1372856
650 4 $a Aerospace engineering. $3 686400
650 4 $a Civil engineering. $3 561339
650 4 $a Computer science. $3 573171
650 4 $a Electrical engineering. $3 596380
650 4 $a Engineering. $3 561152
650 4 $a Linguistics. $3 557829
650 4 $a Transportation. $3 558117
690 $a 0771
690 $a 0538
690 $a 0543
690 $a 0984
690 $a 0544
690 $a 0537
690 $a 0290
690 $a 0709
710 2 $a Hong Kong University of Science and Technology (Hong Kong). $3 1372853
773 0 $t Dissertations Abstracts International $g 83-11B.
790 $a 1223
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29221682