This is already fea­si­ble for a sin­gle robot in a com­pletely sta­tic envi­ron­ment using con­ven­tional SLAM algo­rithms.  How­ever, ware­houses in the real world are any­thing but sta­tic. Peo­ple work in them and change the envi­ron­ment con­tin­u­ously; goods are moved, new obsta­cles come and go. In order to work reli­ably and smoothly, even under these cir­cum­stances, the robots need one more skill: the abil­ity to adapt. To do this, they need as much infor­ma­tion as pos­si­ble about the cur­rent sit­u­a­tion in their envi­ron­ment. With this infor­ma­tion, they can then adapt their paths and oper­ate more efficiently.

Our approach is thus: cloud-based map­ping and local­iza­tion in dynamic ware­house envi­ron­ments. This con­cept is based on the fact that indi­vid­ual robots can col­lect data about their local envi­ron­ment dur­ing oper­a­tion. Each time a robot trav­els through an aisle, it per­ceives its sur­round­ings through sen­sors and cam­eras. If it encoun­ters an obsta­cle en route, it stops and looks for an alter­nate way to reach its des­ti­na­tion. How­ever, if a robot does­n’t have to dis­cover the obsta­cle for itself, it can plan its route accord­ingly and arrive at its des­ti­na­tion faster. So would­n’t it be fan­tas­tic if Robot A could share its knowl­edge with Robot B? Def­i­nitely. That’s why, in coop­er­a­tion with Google Car­tog­ra­pher, we have fur­ther devel­oped a real-time local­iza­tion and map­ping library. In con­crete terms, this means that every robot in a ware­house sends SLAM data to a cloud and simul­ta­ne­ously com­pares its stored inner map of the ware­house with data in the cloud. The robots in the fleet can there­fore share their knowl­edge of the envi­ron­ment and use it for local opti­miza­tions. One could say that the fleet per­ceives its envi­ron­ment col­lec­tively and is con­stantly improv­ing together: Mag­a­zino robots are real team play­ers, just like their developers.