New strategies for combining GNSS and photogrammetric data

The basic concept of integrating GNSS and photogrammetry dates back more than 30 years and for at least the past decade it has been ubiquitous in both aerial and terrestrial mapping. Throughout all its history the basic technique for integrating the two technologies has been the same: GNSS data is post-processed using a Kalman filter yielding positions and these positions are then used as constraining information in a photogrammetric least-squares bundle adjustment. As evidenced by its long and essentially unaltered use the existing strategy works well, nevertheless it has some drawbacks. From a theoretical perspective, the integration is sub-optimal while the information flow is only in the one direction. From an operational perspective, the current approach is unwieldy: (at least) two processing packages are required.;The first strategy introduces two-way communication between the GNSS and photogrammetric processors, while the second strategy introduces measurement-level integration within a single processor. The combined adjustment also allows some more flexible GNSS processing options; for instance, a non-fixed base station or the use of observations when there are less than the 4 normally required. Testing of the inter-processor communication strategy showed that it could help GNSS positioning following signal outages, yet this improvement does not necessarily translate into improved photogrammetric mapping accuracy.;Testing of the combined adjustment demonstrated how photogrammetric control could replace a fixed GNSS base station, and how use of use of GNSS observations during partial signal blockages (when they would otherwise be discarded) could help bound the mapping and exposure position error growth. The combined adjustment testing also showed that the exposure positions derived from a typical aerial block of imagery have too much noise to substantially improve the GNSS positioning; consequently, mapping accuracy also does not improve.;The objective of the research contained within this work is to examine new strategies for integrating GNSS and photogrammetric data that alleviate the aforementioned limitations of the current integration strategy. Specifically, two new integration strategies are introduced, implemented, and tested: (1) Inter-processor communication between a kinematic GNSS Kalman filter and a photogrammetric bundle adjustment. (2) A combined least-squares adjustment of both GNSS and photogrammetric observations.