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dc.contributorDepartment of Mechanical Engineeringen_US
dc.creatorZhong, Yihan-
dc.publisherHong Kong Polytechnic Universityen_US
dc.rightsAll rights reserveden_US
dc.titleLow-cost solid-state LiDAR/Inertial based localization with prior map for autonomous systems in urban canyonsen_US
dcterms.abstractA low-cost and accurate positioning solution is significant for the massive deployment of fully autonomous driving vehicles (ADV). Conventional mechanical LiDAR has proven its performance, but its high cost hinders the massive production of autonomous vehicles. This paper proposes a low-cost LiDAR/Inertial-based localization solution for autonomous systems with prior maps in urban areas. Instead of relying on the costly mechanical LiDAR, this paper proposes utilizing the solid-state LiDAR with the prior map to estimate the vehicle's position by matching the real-time point clouds from the solid-state LiDAR and the prior map using the normal distribution transformation (NDT) algorithm. However, the field of view (FOV) of the solid-state LiDAR is significantly smaller than the conventional mechanical LiDAR, which can easily lead to failure during the NDT map matching. To fill this gap, this paper proposes to exploit the complementariness of the inertial measurement unit (IMU) and the solid-state LiDAR, where the IMU pre-integration provides a coarse but high-frequency initial guess to the map matching. To evaluate the effectiveness of the proposed method in this paper, we collect the dataset in two typical urban scenarios by a pedestrian hand-hold and a vehicle driving condition. The results reveal that the solid-state LiDAR-only based localization is significantly challenged in dynamic scenarios. With the help of the IMU, the robustness of the proposed method is significantly improved, achieving an accuracy of within 0.5 meters. To show the sensitivity of the solid-state LiDAR-based map matching against the initial guess of the state, this paper also presents the convergence results of the map matching under different levels of accuracy in terms of the initial guess.en_US
dcterms.extentviii, 70 pages : color illustrationsen_US
dcterms.isPartOfPolyU Electronic Thesesen_US
dcterms.educationalLevelAll Masteren_US
dcterms.LCSHAutomated vehiclesen_US
dcterms.LCSHLocation-based servicesen_US
dcterms.LCSHRemote sensingen_US
dcterms.LCSHHong Kong Polytechnic University -- Dissertationsen_US
dcterms.accessRightsrestricted accessen_US

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