Efficient and Consistent Vision-aided Inertial Navigation using Line Observations

This paper addresses the problem of estimating the state of a vehicle moving in 3D based on inertial measurements and visual observations of lines. In particular, we investigate the observability properties of the corresponding vision-aided inertial navigation system (VINS) and prove that it has five (four) unobservable degrees of freedom when one (two or more) line(s) is (are) detected. Additionally, we leverage this result to improve the consistency of the extended Kalman filter (EKF) estimator introduced for efficiently processing line observations over a sliding time-window at cost only linear in the number of line features. Finally, we validate the proposed algorithm experimentally using a miniature-size camera and a micro-electromechanical systems (MEMS)-quality inertial measurement unit (IMU).