Dynamic measurement method based on temporal-spatial geometry constraint and optimized motion estimation

Dynamic position and pose measurement in large-scale space has become the fundamental requirement at manufacturing sites. Multi-station photoelectric scanning systems represented by indoor Global Positioning System (iGPS) has better potentials than single-station measurement system such as laser tracker due to their excellent parallel measurement capability and network extensibility. However, influenced by the measurement principle of multi-observation intersection, iGPS suffers from dynamic error due to asynchronous measurements of different stations. In this paper we propose a novel measurement model based on motion estimation. We set a sliding window and construct an optimization problem using the velocity of the target and timestamps of observations from different stations. We validate the performance of our method and compare against existing methods. Our method demonstrates a remarkable improvement in measurement accuracy (as much as 90% of the dynamic errors are removed) and frequency (ten times greater), proving that it is applicable for different conditions.