Latest revision as of 08:28, 2 February 2021

Abstract

While monocular visual odometry has been widely investigated, one of its key issues restrains its broad appliance: the scale drift. To tackle it, we leverage scene inherent information about the ground plane to estimate the scale for usage on Advanced Driver Assistance Systems. The algorithm is conceived so that it is independent of the unscaled ego-motion estimation, augmenting its adaptability to other frameworks. A ground plane estimation using Structure From Motion techniques is complemented by a vanishing point estimation to render our algorithm robust in urban scenarios. The method is evaluated on the KITTI dataset, outperforming state of the art algorithms in areas where urban scenery is dominant.

Original document

The different versions of the original document can be found in:

• http://www.mrt.kit.edu/z/publ/download/IV_Paper_2015_JG_non_ieee.pdf

• http://www.mrt.kit.edu/z/publ/download/IV_Paper_2015_JG_non_ieee.pdf,

https://dblp.uni-trier.de/db/conf/ivs/ivs2015.html#GraterSL15,

https://ieeexplore.ieee.org/document/7225730,

https://dx.doi.org/10.1109/IVS.2015.7225730,

http://ieeexplore.ieee.org/document/7225730,

http://dx.doi.org/10.1109/IVS.2015.7225730,

https://academic.microsoft.com/#/detail/1545092130

• http://xplorestaging.ieee.org/ielx7/7180136/7225648/07225730.pdf?arnumber=7225730,

http://dx.doi.org/10.1109/ivs.2015.7225730