VisualInertial Odometry with Point and Line Features In this paper, we present a tightlycoupled monocular visualinertial navigation system (VINS) using points and lines with degenerate motion analysis for 3D line triangulation. Based on line segment measurements from images, we propose two sliding window based 3D line triangulation algorithms and compare their performance. Analysis of the proposed algorithms reveals 3 degenerate camera motions that cause triangulation failures. Both geometrical interpretation and MonteCarlo simulations are provided to verify these degenerate motions which prevent triangulation. In addition, commonly used line representations are compared through a monocular visual SLAM MonteCarlo simulation. Finally, realworld experiments are conducted to validate the implementation of the proposed VINS system using the xe2x80x9cclosest pointxe2x80x9d line representation.

A GPSaided Omnidirectional VisualInertial State Estimator in Ubiquitous Environments The visualinertial navigation system (VINS) has been a practical approach for state estimation in recent years. In this paper, we propose a general GPSaided omnidirectional visualinertial state estimator capable of operating in ubiquitous environments and platforms. Our system consists of two parts: 1) the preprocessing of omnidirectional cameras, IMU, and GPS measurements, and 2) the sliding window based nonlinear optimization for accurate state estimation. We test our system in different conditions including an indoor office, campus roads, and challenging open water surface. Experiment results demonstrate the high accuracy of our approach than stateoftheart VINSs in all scenarios. The proposed odometry achieves drift ratio less than 0.5% in 1200 m length outdoors campus road in overexposure conditions and 0.65% in open water surface, without a loop closure, compared with a centimeter accuracy GPS reference.