1.0Microsoft Researchhttps://www.microsoft.com/en-us/researchChristopher Meihttps://www.microsoft.com/en-us/research/people/chmei/Adaptive relative bundle adjustment - Microsoft Researchrich600338<blockquote class="wp-embedded-content" data-secret="6lXRws3AcI"><a href="https://www.microsoft.com/en-us/research/publication/adaptive-relative-bundle-adjustment/">Adaptive relative bundle adjustment</a></blockquote><iframe sandbox="allow-scripts" security="restricted" src="https://www.microsoft.com/en-us/research/publication/adaptive-relative-bundle-adjustment/embed/#?secret=6lXRws3AcI" width="600" height="338" title="“Adaptive relative bundle adjustment” — Microsoft Research" data-secret="6lXRws3AcI" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"></iframe><script type="text/javascript">
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It is well known that bundle adjustment is the optimal non-linear least-squares formulation of the simultaneous localization and mapping problem, in that its maximum likelihood form matches the definition of the Cramer Rao Lower Bound. Unfortunately, computing the ML solution is often prohibitively expensive – this is especially true during loop closures, which often necessitate […]