Robust and Accurate Line- and/or Point-Based Pose Estimation without Manhattan Assumptions [chapter]

Yohann Salaün, Renaud Marlet, Pascal Monasse
<span title="">2016</span> <i title="Springer International Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
Usual Structure from Motion techniques based on feature points have a hard time on scenes with little texture or presenting a single plane, as in indoor environments. Line segments are more robust features in this case. We propose a novel geometrical criterion for two-view pose estimation using lines, that does not assume a Manhattan world. We also define a parameterless (a contrario) RANSAC-like method to discard calibration outliers and provide more robust pose estimations, possibly using
more &raquo; ... ts as well when available. Last, we provide quantitative experimental data that illustrate failure cases of other methods and that show how our approach outperforms them, both in robustness and accuracy.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-46478-7_49">doi:10.1007/978-3-319-46478-7_49</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6dkp426hwbdajh55mcrbmt4cdm">fatcat:6dkp426hwbdajh55mcrbmt4cdm</a> </span>
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