Cooperative Image Orientation with Dynamic Objects

Using images to supplement classical navigation solutions purely based on global navigation satellite systems (GNSSs) has the potential to overcome problems in densely built-up areas. These approaches usually assume a static environment; however, this assumption is not necessarily valid in urban areas. Therefore, many approaches delete information stemming from moving objects in a first processing step, but this results in information being lost. In this paper, we present an approach that detects and models so-called dynamic objects based on image sequences and includes these object models into a bundle adjustment. We distinguish dynamic objects that provide information about their position to others (cooperating objects) and those that do not (non-cooperating objects). Dynamic objects that observe the environment with the help of sensors in order to determine their position are called observing objects. In the experiments discussed here, the observing object is equipped with a stereo camera and a GNSS receiver. We show that cooperating objects can have a positive effect on the exterior orientation of the observing object after the bundle adjustment, both in terms of precision and accuracy. However, we found that introducing non-cooperating objects did not result in further improvements, probably because in our case the photogrammetric block was already stable without them due to the large number and good distribution of static tie points.