Feature-based map merging with dynamic consensus on information increments

We study the feature-based map merging problem in robot networks. Each robot observes the environment and builds a local map. Simultaneously, robots communicate and compute the global map of the environment; this communication is range-limited. We propose a dynamic strategy based on consensus algorithms that is fully distributed and does not rely on any particular communication topology. Robots reach consensus on the latest global map, using the increments between their previous and current local maps. Under mild connectivity conditions, our merging algorithm asymptotically converges to the global map. We give proofs of unbiasedness of this global map, at each step and robot. Our approach has been validated using real RGB-D images.