Towards the automatic motion recovery using single-view image sequences acquired from bike

This paper describes the design, implementation and results of the image-based ego-motion estimation algorithm. As a source data the images captured from the bike platform are used. The device is supposed to be a part of a mobile mapping system prototype. Firstly the feature detection and matching is carried out providing the set of characteristic points in all images in the sequence. The 5-point solution based on the Grobner basis is used to solve for essential matrices and to reject outliers. Least-square relative pose model fitting is accomplished using quaternion-based bundle adjustment. In the next step the modified Horn formula is used to recover bike trajectory up to the absolute orientation. Within this step the scene structure recovery is provided in the form of a point cloud. Finally ground control information is used to obtain data geo-referencing and the accuracy analysis. Obtained results provide satisfying robustness and accuracy. However some improvements and development scenarios are suggested.