AbstractVisual-inertial odometry (VIO) is the pose estimation backbone for most AR/VR and autonomous robotic systems today, in both academia industry. However, these are highly sensitive to initialization of key parameters such as sensor biases, gravity direction, metric scale. In practical scenarios where high-parallax or variable acceleration assumptions rarely met (e.g. hovering aerial robot...

Model initialization is a matter of transferring the observed information available at start forecast to model. An optimal generally recognized be able improve climate predictions up few years ahead. However, systematic errors in models make process challenging. When transferred model time, discrepancy between and mean causes drift prediction toward model-biased attractor. Although such drifts ...

A method for improving centroid-based clustering is suggested. The improvement built on diversification of the k-means++ initialization. algorithm claimed to be a better version k-means tested by computational set-up, where dataset size, number features, and clusters are varied. statistics obtained testing have shown that, in roughly 50 % instances cluster, outputs worse results than with rando...