Master Theses (finished)
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Localization of mobile objects in the Absence of GPS/GNSS: A Hybrid 2D-3D ApproachIn today's dynamic landscape of autonomous vehicles and robotics, accurate and real-time localization is imperative. While 3D methods have been employed for vehicle localization, their time-consuming nature poses challenges. This research seeks to a novel hybrid approach, bridging the efficiency of 2D methods with the precision of 3D refinement, to offer a faster and more robust solution for vehicle localization. The primary goal is to investigate and implement a localization method leveraging 2D elevation models and BEV image as well, generated from point clouds acquired by a Mobile Mapping System (MMS) as the map. Additionally, point clouds from various sensors, including LiDAR, will be employed for localization. The methodology initiates rough vehicle localization through feature matching in 2D space, followed by a refinement process in 3D space.This integrated approach is designed to not only expedite the localization process but also enhance accuracy and precision, particularly in challenging environments where GPS/GNSS data is unavailable.Led by: MortazaviTeam:Year: 2024
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Enhancing Point Cloud Localization with Adaptive Weighting Using Classified/Segmented DataThis study focuses on improving point cloud-based localization by developing an adaptive weighting algorithm. The research addresses challenges in dynamic environments by classifying point cloud data into semantic categories, such as static and dynamic objects, and adjusting the localization weights accordingly. The adaptive approach enhances accuracy by prioritizing stable environmental features and reducing the influence of dynamic elements. The work involves point cloud classification, adaptive weighting implementation, and performance evaluation using iterative closest point (ICP) algorithms, with a focus on benchmarking against traditional geometric methods.Led by: MortazaviTeam:Year: 2024
Open Master Theses
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Localization of mobile objects in the Absence of GPS/GNSS: A Hybrid 2D-3D ApproachIn today's dynamic landscape of autonomous vehicles and robotics, accurate and real-time localization is imperative. While 3D methods have been employed for vehicle localization, their time-consuming nature poses challenges. This research seeks to a novel hybrid approach, bridging the efficiency of 2D methods with the precision of 3D refinement, to offer a faster and more robust solution for vehicle localization.Led by: Mortazavi, SesterYear: 2023
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