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- W3091325719 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." @default.
- W3091325719 created "2020-10-08" @default.
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- W3091325719 date "2020-05-19" @default.
- W3091325719 modified "2023-09-27" @default.
- W3091325719 title "Compute-bound and low-bandwidth distributed 3D graph-SLAM" @default.
- W3091325719 doi "https://doi.org/10.1117/12.2558168" @default.
- W3091325719 hasPublicationYear "2020" @default.
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