  This article describes a new approach for distributed 3D SLAM map building.
The key contribution of this article is the creation of a distributed
graph-SLAM map-building architecture responsive to bandwidth and computational
needs of the robotic platform. Responsiveness is afforded by the integration of
a 3D point cloud to plane cloud compression algorithm that approximates dense
3D point cloud using local planar patches. Compute bound platforms may restrict
the computational duration of the compression algorithm and low-bandwidth
platforms can restrict the size of the compression result. The backbone of the
approach is an ultra-fast adaptive 3D compression algorithm that transforms
swaths of 3D planar surface data into planar patches attributed with image
textures. Our approach uses DVO SLAM, a leading algorithm for 3D mapping, and
extends it by computationally isolating map integration tasks from local
Guidance, Navigation, and Control tasks and includes an addition of a network
protocol to share the compressed plane clouds. The joint effect of these
contributions allows agents with 3D sensing capabilities to calculate and
communicate compressed map information commensurate with their onboard
computational resources and communication channel capacities. This opens SLAM
mapping to new categories of robotic platforms that may have computational and
memory limits that prohibit other SLAM solutions.
