Bundle block adjustment of omni-directional images obtained from a ground mobile mapping system

Regarding the increasing demands for high quality spatial information, many researchers have emphasized the need for multi-sensor/platform integration for more rapid and economical generation of such spatial data. Most systems employing a set of frame cameras may have suffered from their small fields of view and poor base-distance ratio. These limitations can be significantly improved by employing an omni-directional camera that is capable of acquiring images in every direction. Using a GMMS integrated with this camera, we can enlarge the mapping coverage and observe objects from a variety of directions and positions. Bundle Block Adjustment (BBA) is one of the existing georeferencing methods to determine the exterior orientation parameters of two or more images. In this study, by expanding the concept of the traditional BBA method, we attempt to develop a mathematical model of BBA for omni-directional images with GPS/INS data and Ground Control Points (GCPs). The proposed mathematical model includes two main parts; observation equations based on the collinearity equations newly derived for omni-directional images and stochastic constraints imposed from GPS/INS data and GCPs. We also report the experimental results from the application of our proposed BBA to the real data obtained mainly in urban areas. With the different combinations of the constraints, we applied four slightly different types of mathematical models. The type where only GCPs are used as the constraints provides the most accurate results, less than 5 cm of RMSE in the estimated ground point coordinates. In future, we plan to perform more sophisticated lens calibration for the omni-directional camera to improve the georeferencing accuracy of omni-directional images. These georeferenced omni-directional images can be effectively utilized for city modelling, particularly autonomous texture mapping for realistic street view.