טכניון מכון טכנולוגי לישראל
הטכניון מכון טכנולוגי לישראל - בית הספר ללימודי מוסמכים  
M.Sc Thesis
M.Sc StudentBriskin Gil
SubjectEstimating Pose and Motion Using Bundle Adjustment and
Digital Elevation Model Consraints
DepartmentDepartment of Computer Science
Supervisors Professor Ehud Rivlin
Dr. Hector Rotstein
Full Thesis textFull thesis text - English Version


Abstract

Pose and motion estimation of a calibrated camera is a common application in the photogrammetric world. In most cases this is solved by combining motion estimation with connections between the images taken by the camera and external geographical data such as Global Positioning System (GPS), Orthophoto, Digital Terrain Model etc.

While the motion estimation can be solved automatically using only the connections between the images, the connections to the geographical data are generated manually or by using geographical measurements, such as GPS signals of the camera’s motion.

The contribution of this thesis is a new constraint added to a pose and motion estimation algorithm. We propose to integrate the Digital Terrain Model (DTM) into the Bundle Adjustment framework. For that, the terrain was approximated to a differentiable second order function and new constraints were added to the Bundle Adjustment that minimized the distance between the 3D points and the terrain approximation. A framework that solves the pose and motion estimation with the new constraints using inaccurate initial guess was added. We showed that under certain conditions, the proposed method can replace other constraints based on geographical measurements, such as GPS signals on camera’s motion, and by that be the first method that solves the pose and motion estimation of a sequence of images using only DTM and without additional geographical source. Our method has several advantages: The generated 3D structure is more accurate when the DTM constraints are added to the Bundle Adjustment, specifically for Ill-constraint points. In addition, DTM is available worldwide, can be acquired offline and is resistant to signal distortion and blocking as opposed to other measurements such as GPS.