Accelerated Visual Inertial Navigation via Fragmented Structure Updates

Tightly coupled Visual-Inertial 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 co-visible feature points from across several different cameras efficiently split, categorized into groups, and then analyzed using a machine-learning inspired frequent-pattern 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 system's 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 run-time considerations the Raspberry Pi was only competitive in regards to other optimization methodologies, but our algorithm displays remarkable accuracy on a more powerful platform.