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Simultaneous Localization And Mapping (SLAM) is one of the most fundamental and essential topics in robotics research. SLAM is a task for a robot to perceive the environment and localize itself based on inputs from its on-board sensors. The robot is also supposed to construct a map of the surrounding environment for subsequent task planning and collision avoidance. As the robotic industry develops over the past decades, there are more applications depending on the performance of the SLAM system. However, a good SLAM system needs to meet the following requirements. First of all, many advanced robotic applications require localization accuracy at sub-meter level or even centimeter level, e.g., precision landing for Unmanned Aerial Vehicles (UAVs) and auto charging for Automated Guided Vehicles (AGVs). Secondly, the implementation of SLAM extends from indoor AGVs to outdoor autonomous driving cars or UAVs. As the robots move faster, the localization needs to be real-time as well. Any delay in localization result may lead to serious safety issues such as object collision or car accident. Lastly, the robotic applications are expanding from static to dynamic environments, from simple to complex environments, from short term to long term operation, etc. The SLAM framework is supposed to provide reliable localization under different scenarios and be robust to environmental changes. However, mobile robots often have limited computational resources to achieve a good SLAM performance. Motivated by this challenge, this thesis presents a unified SLAM framework with high flexibility, practicality and stability for autonomous vehicles …