Automatically smoothing camera pose using cross validation for sequential vision-based 3D mapping

Building an accurate three dimensional map is an important task for autonomous localisation and navigation. In a sequential approach to reconstruction from video streams, we show how adding prior knowledge about camera motion improves reconstruction accuracy, obtaining a more precise trajectory estimation and preventing failures over time. We add a smoothing penalty on camera trajectory and the smoothing parameter, usually fixed by trial and error, is automatically estimated using Cross-Validation. The method is substantiated by experimental results on synthetic and real data. They show that it improves accuracy and stability in the reconstruction process, preventing several failure cases.