Static Map

Static map creation focuses on building accurate and robust representations of the environment, excluding dynamic elements like moving objects and people. Current research emphasizes efficient algorithms for handling large-scale datasets, often employing techniques like incremental mapping, graph optimization, and uncertainty modeling, sometimes incorporating deep learning architectures such as convolutional neural networks for semantic segmentation and object recognition. These advancements are crucial for applications ranging from autonomous navigation and robotics to large-scale surveying and urban planning, improving the accuracy, efficiency, and scalability of map generation.