Local Exponential Maps: Towards Massively Distributed Multi-robot Mapping
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We present a novel paradigm for massively distributed, large-scale multi-robot mapping. Our goal is to explore techniques that can support continuous mapping over an indefinite amount of time. We argue that to scale to city or even global scales the concept of a single globally consistent map has to be abandoned, and present an infrastructure-supported solution where most of the inference and map-maintenance is done on local "map-servers", rather than on the robot itself. The main technical contribution in the paper is a factor-graph-based scheme for making this possible, and a novel local map representation, local exponential maps, that enable indefinite map updates while remaining self-consistent over time. We present initial experimental results both in simulation and using real data, although a full-scale deployment and evaluation of the technique is left for future work.