Indoor Mapping

Indoor mapping aims to create accurate and comprehensive representations of indoor spaces, crucial for applications like robotics, autonomous navigation, and virtual reality. Current research focuses on improving the accuracy and efficiency of map generation using various sensor fusion techniques (e.g., combining LiDAR, cameras, and inertial measurement units) and advanced algorithms such as transformers and Kalman filters to process data from diverse sources, including point clouds and images. These advancements address challenges like dynamic environments, limited visibility, and the need for real-time performance, ultimately leading to more robust and reliable indoor maps for a wide range of applications.