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The method known as simultaneous localization and mapping, or SLAM, is used to determine the movements of sensors in the surroundings as well as the 3D structure of an unidentified area. This method was first proposed in robotics to accomplish autonomous control of robots. Subsequently, SLAM-based applications have expanded significantly, including self-driving automobiles, augmented reality (AR)-based visualization, and computer vision-based online 3D modeling. Then, SLAM-based applications have expanded significantly, including self-driving automobiles, augmented reality (AR)-based visualization, and computer vision-based online 3D modeling. This paper synthesizes some other researchers' experimental process and conclusions, and discusses the practicability and feasibility of SLAM technology in automotive autonomous driving. It includes the introduction of SLAM technology, the application of SLAM technology in automobile autonomous driving and the shortcomings and solutions of SLAM technology. A large number of experimental research results have proved the feasibility of this technology for this field, which also means that SLAM technology can play a huge role in the field of automotive autonomous driving.
Haoyu Wei (Tue,) studied this question.