Compute-bound and low-bandwidth distributed 3D graph-SLAM

Jincheng Zhang; Andrew R. Willis; Jamie Godwin

doi:10.1117/12.2558168

19 May 2020 Compute-bound and low-bandwidth distributed 3D graph-SLAM

Jincheng Zhang, Andrew R. Willis, Jamie Godwin

Proceedings Volume 11425, Unmanned Systems Technology XXII; 1142504 (2020) https://doi.org/10.1117/12.2558168
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

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 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, 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 planes. The joint effect of these contributions allows agents with 3D sensing capabilities to calculate and communicate compressed map information commensurate with their on-board 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.

Citation Download Citation

Jincheng Zhang, Andrew R. Willis, and Jamie Godwin "Compute-bound and low-bandwidth distributed 3D graph-SLAM", Proc. SPIE 11425, Unmanned Systems Technology XXII, 1142504 (19 May 2020); https://doi.org/10.1117/12.2558168

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