Session SLAM

Multi-session SLAM aims to create a consistent, global map from multiple, independent mapping sessions, overcoming the limitations of single-session approaches that suffer from drift and inability to handle large-scale environments. Current research focuses on robust methods for aligning these disparate sessions, often employing techniques like differentiable pose optimization, integration of Building Information Models (BIMs) for prior map information, and advanced visual-inertial odometry with improved IMU pre-integration. These advancements are crucial for improving the accuracy and reliability of robotic mapping in challenging real-world scenarios, particularly in construction and large-scale indoor environments, as demonstrated by recent benchmark challenges.