Accelerated Visual Inertial Navigation via Fragmented Structure Updates Tightly coupled VisualInertial Navigation System (VINS) implementations have proven their superiority due to their ability to jointly optimize all state variables. However, this joint optimization is considered as a computational bottleneck within the system, and thus many traditional VINS can only be implemented on platforms containing powerful processors. In this work, we show a significant reduction in the computational burden of optimization can be achieved through the use of fragments; a set of covisible feature points from across several different cameras efficiently split, categorized into groups, and then analyzed using a machinelearning inspired frequentpattern growth algorithm. Furthermore, we use a reduced continuous representation during preintegration for better accuracy while requiring less computational resources. We validate our algorithm in datasets, showing that the derivation is not only more accurate, but requires significantly less computational resources. When tested on a Raspberry Pi, our implementation was able to track the systemxe2x80x99s state in nearly 20 frames per second using only a single CPU core. Testing on another low power module shows a power draw of around 800 mW. Due to runtime considerations the Raspberry Pi was only competitive in regards to other optimization methodologies, but our algorithm displays remarkable accuracy on a more powerful platform.

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.