SCAAT: incremental tracking with incomplete information

Abstract

We present a promising new mathematical method for tracking a user's pose (position and orientation) for interactive computer graphics.The method, which is applicable to a wide variety of both commercial and experimental systems, improves accuracy by properly assimilating sequential observations, filtering sensor measurements, and by concurrently autocalibrating source and sensor devices.It facilitates user motion prediction, multisensor data fusion, and higher report rates with lower latency than previous methods.Tracking systems determine the user's pose by measuring signals from low-level hardware sensors.For reasons of physics and economics, most systems make multiple sequential measurements which are then combined to produce a single tracker report.For example, commercial magnetic trackers using the SPASYN ( Space Synchro ) system sequentially measure three magnetic vectors and then combine them mathematically to produce a report of the sensor pose.Our new approach produces tracker reports as each new lowlevel sensor measurement is made rather than waiting to form a complete collection of observations.Because single observations under-constrain the mathematical solution, we refer to our approach as single-constraint-at-a-time or SCAAT tracking.The key is that the single observations provide some information about the user's state, and thus can be used to incrementally improve a previous estimate.We recursively apply this principle, incorporating new sensor data as soon as it is measured.With this approach we are able to generate estimates more frequently, with less latency, and with improved accuracy.We present results from both an actual implementation, and from extensive simulations.