Visual-Inertial SLAM
SLAM: Simultaneous Localization and Mapping
Overview
Visual-inertial SLAM uses camera images and inertial measurements to track a robot’s motion while building a map, helping it navigate when GPS is unavailable. Systems commonly combine feature tracking or learned visual front ends with filtering or sliding-window optimization, bundle adjustment, and loop closure; newer approaches also use event cameras, multiple cameras, and learned depth or scene representations. Benchmarks such as EuRoC and TUM-VI test trajectory accuracy, while real-world settings including UAV flights, construction sites, forests, and underwater environments expose different sensing challenges. A central tension is balancing accuracy and robustness to motion, poor lighting, textureless or dynamic scenes, and sensor failures against limited onboard computation and power.
Latest papers
29 papers8 papers in the last four weeks, up 100% on the four weeks before. 0.0% of all new papers.