Feature-based map merging with dynamic consensus on information increments

Abstract

Abstract-We study the problem of feature-based map merging in robot networks. Along its operation, each robot observes the environment and builds and maintains a local map. Simultaneously, each robot communicates and computes the global map of the environment. The communication between the robots 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 map increments between the previous and the current time steps. Under mild connectivity conditions on the communication graph, our merging algorithm asymptotically converges to the global map. We give proofs of unbiasedness and consistency of this global map for all the robots, at each iteration. The proposed approach has been experimentally validated using real RGB-D images.

Tutorial on Dynamic Average Consensus: The Problem, Its Applications, and the Algorithms

“…Similar to the dynamic average consensus algorithm (19), (24) does not require any specific initialization. The technical approach used in [54] to study the convergence of (24) is based on singular perturbation theory [55,Chapter 11], which results in a guaranteed convergence to an -neighborhood of u avg (t) for small values of ∈ R >0 .…”

“…An alternative that has previously gained interest [20,21,5,22,23] is to employ distributed algorithmic solutions that have each sensor station maintain a local filter to process its local measurements and fuse them with the estimates of its neighbors. Some work [20,24,25] employ dynamic average consensus to synthesize distributed implementations of the Kalman filter. For instance, one of the early solutions for distributed minimum variance estimation, has each agent maintain a local copy of the propagation filter (4) and employ a dynamic average consensus algorithm to generate the coupling time-varying terms 1…”

A novel map-merging technique for occupancy grid-based maps using multiple robots: a semantic approach

“…These advantages have led to occupancy-based maps being used in this study. In a similar manner to different map types, map merging can also be characterized by various approaches as based on features, iterative closest points (ICP), or overalldirect optimization [21][22][23]. This paper uses feature-based methods to match the regions of points of interest because the environment has sufficient properties for extraction.…”