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Dynamic Covariance Scaling for Robust Map Optimization
Pratik Agarwal, Gian Diego Tipaldi, Luciano Spinello, Cyrill Stachniss, Wolfram Burgard Workshop on robust and Multimodal Inference in Factor Graphs (ICRA) 2013
agarwal13_icra_ws.pdf
Notes: Developing the perfect SLAM front-end that produces
graphs which are free of outliers is hard to achieve due
to perceptual aliasing. Converging to the correct solution is
challenging for non-linear error minimization SLAM techniques
even in the absence of outliers, if the initial guess is far away
from the correct solution. Therefore, optimization back-ends
need to be resilient to outliers resulting from an imperfect
front-end as well as be robust to bad initialization. In this
paper, we present dynamic covariance scaling, a novel approach
for effective optimization of constraint networks under the
presence of outliers and bad initial guess. The key idea is
to use a robust function that generalizes classical gating and
down-weights outliers without compromising convergence speed.
Compared to recently published state-of-the-art methods, we
obtain a substantial speed-up without increasing overheads.
BibTeX:
@inproceedings{agarwal13icraws,
author = {Pratik Agarwal and Gian Diego Tipaldi and Luciano Spinello and Cyrill Stachniss and Wolfram Burgard},
title = {Dynamic Covariance Scaling for Robust Robotic
Mapping},
booktitle = {Workshop on robust and Multimodal Inference in
Factor Graphs, {ICRA}},
year = 2013,
month = may,
doi = {10.1109/ICRA.2013.6630557},
url = {http://ais.informatik.uni-freiburg.de/publications/papers/agarwal13_icra_ws.pdf},
keyword = {SLAM, Robust optimization},
address = {Karlsruhe, Germany}
}
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