SLAM Baseline

SLAM (Simultaneous Localization and Mapping) baselines serve as fundamental reference points for evaluating new algorithms in robotics and computer vision, aiming to accurately estimate a robot's pose and build a map of its environment simultaneously. Current research focuses on improving robustness in challenging conditions (e.g., low-light, thermal imagery) and enhancing accuracy through techniques like Gaussian splatting for dense mapping and incorporating semantic information (e.g., object recognition) for improved registration. These advancements are crucial for enabling reliable autonomous navigation in diverse and complex environments, with applications ranging from autonomous vehicles to planetary exploration.