We propose a multi robot SLAM approach that uses 3D objects as landmarks for localization and mapping. The approach is fully distributed in that the robots only communicate during rendezvous and there is no centralized server gathering the data. Moreover, it leverages local computation at each robot (e.g., object detection and object pose estimation) to reduce the communication burden. We show that object-based representations reduce the memory requirements and information exchange among robots, compared to point-cloud-based representations; this enables operation in severely bandwidth-constrained scenarios. We test the approach in simulations and field tests, demonstrating its advantages over related techniques: our approach is as accurate as a centralized method, scales well to large teams, and is resistant to noise.
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