Kullback-leibler divergence based graph pruning in robotic feature mapping

@article{Wang2013KullbackleiblerDB,
  title={Kullback-leibler divergence based graph pruning in robotic feature mapping},
  author={Yue Wang and Rong Xiong and Qianshan Li and Shoudong Huang},
  journal={2013 European Conference on Mobile Robots},
  year={2013},
  pages={32-37}
}
In pose feature graph simultaneous localization and mapping, the robot poses and feature positions are treated as graph nodes and the odometry and observations are treated as edges. The size of the graph exerts an important influence on the efficiency of the graph optimization. Conventionally, the size of the graph is kept small by discarding the current frame if it is not spatially far enough from the previous one or not informative enough. However, these approaches cannot discard the already… CONTINUE READING
7 Citations
20 References
Similar Papers

References

Publications referenced by this paper.
Showing 1-10 of 20 references

A tutorial on graph - based slam

  • R. Kümmerle G. Grisetti, C. Stachniss, W. Burgard
  • Intelligent Transportation Systems Magazine…
  • 2010

Similar Papers

Loading similar papers…