This thesis deals with estimating position and orientation in real-time, using measurements from vision and inertial sensors. A system has been developed to solve this problem in unprepared environments, assuming that a map or scene model is available. Compared to ‘camera-only’ systems, the combination of the complementary sensors yields an accurate and robust system which can handle periods with uninformative or no vision data and reduces the need for high frequency vision updates. The system achieves real-time pose estimation by fusing vision and inertial sensors using the framework of nonlinear state estimation for which state space models have been developed. The performance of the system has been evaluated using an augmented reality application where the output from the system is used to superimpose virtual graphics on the live video stream. Furthermore, experiments have been performed where an industrial robot providing ground truth data is used to move the sensor unit. In both cases the system performed well. Calibration of the relative position and orientation of the camera and the inertial sensor turn out to be essential for proper operation of the system. A new and easy-to-use algorithm for estimating these has been developed using a gray-box system identification approach. Experimental results show that the algorithm works well in practice.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-11842 |
| Date | January 2008 |
| Creators | Hol, Jeroen Diederik |
| Publisher | Linköpings universitet, Reglerteknik, Linköpings universitet, Tekniska högskolan, Institutionen för systemteknik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Licentiate thesis, monograph, info:eu-repo/semantics/masterThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | Linköping Studies in Science and Technology. Thesis, 0280-7971 ; 1370 |
Page generated in 0.002 seconds