Visual and lidar-based SLAM by variational bayesian methods - Jiang, Xiaoyue (Nanyang Technological University, 2020)
Autonomous vehicles, such as self-driving cars, unmanned aerial vehicles, planetary rovers, and autonomous underwater vehicles, have found applications in a wide variety of domains in recent years, from transportation to searching, rescue, and military. A major challenge for designing autonomous vehicles lies in understanding the surroundings via their onboard sensors. A technology to bridge over this difficulty is Simultaneous Localization and Mapping (SLAM). SLAM continuously scans and ``learns'' about the operating environment, allowing vehicles to augment the physical world with digital content depending on their locations within this space.