  This work proposes a non-iterative, scalable, and statistically optimal way
to triangulate called \texttt{LOSTU}. Unlike triangulation algorithms that
minimize the reprojection ($L_2$) error, LOSTU will still provide the maximum
likelihood estimate when there are errors in camera pose or parameters. This
generic framework is used to contextualize other triangulation methods like the
direct linear transform (DLT) or the midpoint. Synthetic experiments show that
LOSTU can be substantially faster than using uncertainty-aware
Levenberg-Marquardt (or similar) optimization schemes, while providing results
of comparable precision. Finally, LOSTU is implemented in sequential
reconstruction in conjunction with uncertainty-aware pose estimation, where it
yields better reconstruction metrics.
