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A core component of all Structure from Motion (SfM) approaches is bundleadjustment. As the latter is a computational bottleneck for larger blocks,parallel bundle adjustment has become an active area of research. Particularly,consensus-based optimization methods have been shown to be suitable for thistask. We have extended them using covariance information derived by theadjustment of individual three-dimensional (3D) points, i.e., "triangulation"or "intersection". This does not only lead to a much better convergencebehavior, but also avoids fiddling with the penalty parameter of standardconsensus-based approaches. The corresponding novel approach can also be seenas a variant of resection / intersection schemes, where we adjust duringintersection a number of sub-blocks directly related to the number of threadsavailable on a computer each containing a fraction of the cameras of the block.We show that our novel approach is suitable for robust parallel bundleadjustment and demonstrate its capabilities in comparison to the basicconsensus-based approach as well as a state-of-the-art parallel implementationof bundle adjustment. Code for our novel approach is available on GitHub:https://github.com/helmayer/RPBA

RPBA -- Robust Parallel Bundle Adjustment Based on Covariance Information