Simultaneous Mapping

Simultaneous mapping, encompassing tasks like simultaneous localization and mapping (SLAM) and related problems, aims to concurrently build a map of an environment while simultaneously determining the location and orientation of a sensor within that environment. Current research focuses on improving the robustness and efficiency of these processes, particularly in dynamic and challenging environments, employing techniques like factor graph optimization, deep learning models (including diffusion models and transformer-based architectures), and novel fusion strategies for integrating data from multiple sensors (e.g., LiDAR, cameras). These advancements have significant implications for autonomous navigation in robotics, improved accuracy in various mapping applications (e.g., urban environments, railways), and enhanced performance in image and signal processing tasks such as image restoration and medical image segmentation.