Collaborative Visual SLAM Using Compressed Feature Exchange

In the field of robotics, collaborative simultaneous localization and mapping (SLAM) is still a challenging problem. The exploration of unknown large-scale environments benefits from sharing the work among multiple agents possibly equipped with different abilities, such as aerial or ground-based vehicles. In this letter, we specifically address data-efficiency for the exchange of visual information in a collaborative visual SLAM setup. For efficient data exchange, we extend a compression scheme for local binary features by two additional modes providing support for local features with additional depth information and an inter-view coding mode exploiting the spatial relations between views of a stereo camera system. To demonstrate the coding framework, we use a centralized system architecture based on ORB-SLAM2, where energy-constrained agents extract local binary features and send a compressed version over a network to a more powerful agent, which is capable of running several visual SLAM instances in parallel. We exploit the information from other agents by detecting the overlap between already mapped areas and subsequent merging of the maps. Henceforth, the participants contribute to a joint representation and benefit from shared map information. We show a reduction in terms of data-rate by 70.8% using the feature compression and a reduction in absolute trajectory error by 53.7% using the collaborative mapping strategy with three agents on the well-known KITTI dataset. For the benefit of the community, we provide a public version of the source code.