I work in the intersection of these, though primarily from SLAM background. The overlap between these communities has been increasing in the past two years. While I think this is great, I also see that a lot of people working in the field try to take Deep Learning as a 'one-stop' approach to solving SLAM. This is worrying. Any particular methodology, if embraced without understanding pros/cons, can lead to stagnation and local maxima. It was CRFs before that, and Factor Graphs, Particle Filters, or EKF Slam before that.