Learning error models for graph SLAM
Christophe Reymann,Simon Lacroix,Christophe Reymann,Simon Lacroix
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 trajector...
