Scene Flow-Based Environment 3D Digitalization for Mobile Robot Navigation

摘要

Abstract This paper presents an effective 3D digitalization technique to reconstruct an accurate and reliable 3D environment model from multi-view stereo for an environment-learning mobile robot. The novelty of this paper lies in the introduction of nonrigid motion analysis to stereo reconstruction routine. In our proposed scheme, reconstruction task is decoupled into two stages. Firstly, range depth of feature points is recovered and in turn is used for building a polygonal mesh and secondly, projection feedback on comparison views, which is generated on assumption of the established coarse mesh model, is carefully introduced to deform the primitive mesh model so as to improve its quality dramatically. The discrepancy of observation on comparison views and the corresponding predictive feedback is quantitatively evaluated by optical flow field and is employed to derive the corresponding scene flow vector field subsequently, which is then used for surface deformation. As optical flow vector field estimation outperforms traditional dense disparity for its inherent advantage of being robust to illumination change and being optimized and smoothed in global sense, the deformed surface can be improved in accuracy, which is validated by experimental results.