Visual-Inertial Odometry
Also known as VIO
Overview
Visual-inertial odometry (VIO) estimates a moving camera’s position and orientation by combining visual constraints with high-rate inertial measurements, enabling metric navigation when GPS is unavailable. Systems commonly use filtering methods such as MSCKF and error-state Kalman filters, or sliding-window and factor-graph optimization with IMU preintegration, feature tracking, and careful initialization. Newer approaches add learned feature tracking or depth, uncertainty-aware weighting, and continuous-time fusion; they are evaluated on benchmarks such as EuRoC, TUM-VI, and KITTI, as well as in specialized environments. A central challenge is improving robustness to poor lighting, dynamic scenes, sensor outages, and degenerate motion while maintaining observability, estimator consistency, and real-time operation on embedded hardware.
Latest papers
57 papers11 papers in the last four weeks, level with the four weeks before. 0.1% of all new papers.