Visual Inertial SLAM

Visual-inertial SLAM (simultaneous localization and mapping) integrates data from cameras and inertial measurement units (IMUs) to build 3D maps of an environment while simultaneously tracking the robot's position within it. Current research emphasizes improving accuracy and robustness, particularly in challenging conditions like low light, fast motion, and dynamic environments, through techniques such as deep learning feature extraction, advanced optimization algorithms (e.g., bundle adjustment, moving horizon estimation), and the incorporation of additional sensor data (e.g., magnetometers). This technology is crucial for autonomous navigation in robotics, particularly for applications like aerial vehicles, underwater exploration, and autonomous driving, enabling safer and more efficient operation in unstructured environments.