State of the Art SLAM

Simultaneous Localization and Mapping (SLAM) aims to build maps of an environment while simultaneously tracking a robot's location within it. Current research emphasizes improving SLAM's accuracy, robustness, and efficiency across diverse scenarios, focusing on advancements in LiDAR-based and visual SLAM, including hybrid approaches combining direct and indirect methods, and the use of deep learning for tasks like depth estimation and dynamic object removal. These improvements are driven by the need for reliable and resource-efficient SLAM in applications ranging from autonomous robots in challenging environments (e.g., construction sites, forests, and underground spaces) to assistive robots performing precise tasks. The development of comprehensive benchmark datasets and standardized evaluation metrics is also a key focus, enabling more rigorous comparisons of different SLAM algorithms.