  With the wide penetration of smart robots in multifarious fields,
Simultaneous Localization and Mapping (SLAM) technique in robotics has
attracted growing attention in the community. Yet collaborating SLAM over
multiple robots still remains challenging due to performance contradiction
between the intensive graphics computation of SLAM and the limited computing
capability of robots. While traditional solutions resort to the powerful cloud
servers acting as an external computation provider, we show by real-world
measurements that the significant communication overhead in data offloading
prevents its practicability to real deployment. To tackle these challenges,
this paper promotes the emerging edge computing paradigm into multi-robot SLAM
and proposes RecSLAM, a multi-robot laser SLAM system that focuses on
accelerating map construction process under the robot-edge-cloud architecture.
In contrast to conventional multi-robot SLAM that generates graphic maps on
robots and completely merges them on the cloud, RecSLAM develops a hierarchical
map fusion technique that directs robots' raw data to edge servers for
real-time fusion and then sends to the cloud for global merging. To optimize
the overall pipeline, an efficient multi-robot SLAM collaborative processing
framework is introduced to adaptively optimize robot-to-edge offloading
tailored to heterogeneous edge resource conditions, meanwhile ensuring the
workload balancing among the edge servers. Extensive evaluations show RecSLAM
can achieve up to 39% processing latency reduction over the state-of-the-art.
Besides, a proof-of-concept prototype is developed and deployed in real scenes
to demonstrate its effectiveness.
