Adaptive relative bundle adjustment

@inproceedings{Sibley2009AdaptiveRB,
  title={Adaptive relative bundle adjustment},
  author={Dieter Sibley and Christopher Mei and Ian D. Reid and Paul Newman},
  booktitle={Robotics: Science and Systems},
  year={2009}
}
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 adjusting all parameters in a loop. In this paper we note that it is precisely the choice of a single privileged… CONTINUE READING

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