A fast bilinear structure from motion algorithm using a video sequence and inertial sensors.
In this paper, we study the benefits of the availability of a specific form of additional information—the vertical direction (gravity) and the height of the camera, both of which can be conveniently measured using inertial sensors and a monocular video sequence for 3D urban modeling. We show that in the presence of this information, the SfM equations can be rewritten in a bilinear form. This allows us to derive a fast, robust, and scalable SfM algorithm for large scale applications. The SfM algorithm developed in this paper is experimentally demonstrated to have favorable properties compared to the sparse bundle adjustment algorithm. We provide experimental evidence indicating that the proposed algorithm converges in many cases to solutions with lower error than state-of-art implementations of bundle adjustment. We also demonstrate that for the case of large reconstruction problems, the proposed algorithm takes lesser time to reach its solution compared to bundle adjustment. We also present SfM results using our algorithm on the Google StreetView research data set.
Ramachandran
Mahesh
M
Qualcomm Inc., 5775 Morehouse Dr., San Diego, CA 92121-1714, USA. maheshr@umiacs.umd.edu
Veeraraghavan
Ashok
A
Chellappa
Rama
R
D000465:Algorithms; D001185:Artificial Intelligence; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D016247:Information Storage and Retrieval / Q000379:methods; D009038:Motion; D010363:Pattern Recognition, Automated / Q000379:methods; D014741:Video Recording / Q000379:methods