It is highly desired that modern navigational systems work accurately and reliably not only in the situations where a GPS signal is available but also in the situations where GPS signal is not present or is artificially jammed. In many GPS restricted situations, such as indoors, caves, canyons or GPS jamming situations, traditional navigation systems fail to operate. Nowadays, many researchers propose multiple solutions to overcome these limitations. Amongst different solutions for solving GPS-denied navigation, Visual-Inertial Odometry (VIO) gets significant attention in the research community. However, due to significant computational requirements and insufficient robustness while handling complex real life situations, only a small subset of the proposed solutions can provide desirably accurate results and be considered for applications where acceptable Size, Weight, and Power (SWaP) are limited. In this paper, we compare accuracy and robustness of several popular, open-source algorithms and Commercial Off-The-Shelf (COTS) VIO systems potentially suitable for SWaP-limited platforms and applicable for wearable-type applications.
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