Learning error models for graph SLAM

Christophe Reymann; Simon Lacroix

ICRA 2020poster1 citations

Learning error models for graph SLAM

Abstract

Following recent developments, this paper investigates the possibility to predict uncertainty models for monocular graph SLAM using topological features of the problem. An architecture to learn relative (i.e. inter-keyframe) uncertainty models using the resistance distance in the covisibility graph is presented. The proposed architecture is applied to simulated UAV coverage path planning trajectories and an analysis of the approaches strengths and shortcomings is provided.

BibTeX

@inproceedings{icra2020_learningerrormod,
  title = {Learning error models for graph SLAM},
  author = {Christophe Reymann and Simon Lacroix},
  booktitle = {ICRA 2020},
  year = {2020}
}