Dense Mapping

Dense mapping aims to create detailed, three-dimensional models of environments using sensor data, primarily focusing on achieving real-time performance and high accuracy even in challenging conditions like low texture or dynamic scenes. Current research emphasizes the integration of multiple sensor modalities (e.g., cameras, LiDAR, IMUs) with deep learning techniques, often employing neural implicit representations (like NeRFs and implicit signed distance fields) or other advanced algorithms such as bundle adjustment and Gaussian splatting for efficient and robust map construction. These advancements are crucial for applications in robotics (autonomous navigation, planning), augmented/virtual reality, and autonomous driving, enabling more sophisticated interaction with and understanding of the surrounding environment.