Finding good cycle constraints for large scale multi-robot SLAM

In this paper we describe an algorithm to compute cycle constraints that can be used in many graph-based SLAM algorithms; we exemplify it in hierarchical SLAM. Our algorithm incrementally computes the minimum cycle basis of constraints from which any other cycle can be derived. Cycles in this basis are local and of minimum length, so that the associated cycle constraints have less linearization problems. This also permits to construct regional maps, that is, it makes possible efficient and accurate intermediate mapping levels between local maps and the whole global map. We have extended our algorithm to the multi-robot case. We have tested our methodology using the Victoria Park data set with satisfactory results.