Although Structure-from-Motion (SfM) as a maturing technique has been widely
used in many applications, state-of-the-art SfM algorithms are still not robust
enough in certain situations. For example, images for inspection purposes are
often taken in close distance to obtain detailed textures, which will result in
less overlap between images and thus decrease the accuracy of estimated motion.
In this paper, we propose a LiDAR-enhanced SfM pipeline that jointly processes
data from a rotating LiDAR and a stereo camera pair to estimate sensor motions.
We show that incorporating LiDAR helps to effectively reject falsely matched
images and significantly improve the model consistency in large-scale
environments. Experiments are conducted in different environments to test the
performance of the proposed pipeline and comparison results with the
state-of-the-art SfM algorithms are reported.Comment: 6 pages plus reference. Work has been submitted to ICRA 202