It is widely recognized that inertial sensors, in particular gyroscopes, can improve the latency and accuracy of orientation tracking by fusing the inertial measurements with data from other sensors. In our previous work, we introduced the concepts of spatial relationship graphs and spatial relationship patterns to formally model multi-sensor tracking setups and derive valid applications of well-known algorithms in order to infer new spatial relationships for tracking and calibration. In this work, we extend our approach by providing additional spatial relationship patterns that transform incremental rotations and add gyroscope alignment and fusion. The usefulness of the resulting tracking configurations is evaluated in two different scenarios with both inside-out and outside-in tracking.     «

It is widely recognized that inertial sensors, in particular gyroscopes, can improve the latency and accuracy of orientation tracking by fusing the inertial measurements with data from other sensors. In our previous work, we introduced the concepts of spatial relationship graphs and spatial relationship patterns to formally model multi-sensor tracking setups and derive valid applications of well-known algorithms in order to infer new spatial relationships for tracking and calibration. In thi...     »