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Sensors 2019, 19(3), 636;

Embedded Processing and Compression of 3D Sensor Data for Large Scale Industrial Environments

Department of Engineering Sciences, University of Agder, 4879 Grimstad, Norway
Author to whom correspondence should be addressed.
Received: 20 December 2018 / Revised: 20 January 2019 / Accepted: 29 January 2019 / Published: 2 February 2019
(This article belongs to the Special Issue Depth Sensors and 3D Vision)
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This paper presents a scalable embedded solution for processing and transferring 3D point cloud data. Sensors based on the time-of-flight principle generate data which are processed on a local embedded computer and compressed using an octree-based scheme. The compressed data is transferred to a central node where the individual point clouds from several nodes are decompressed and filtered based on a novel method for generating intensity values for sensors which do not natively produce such a value. The paper presents experimental results from a relatively large industrial robot cell with an approximate size of 10 m × 10 m × 4 m. The main advantage of processing point cloud data locally on the nodes is scalability. The proposed solution could, with a dedicated Gigabit Ethernet local network, be scaled up to approximately 440 sensor nodes, only limited by the processing power of the central node that is receiving the compressed data from the local nodes. A compression ratio of 40.5 was obtained when compressing a point cloud stream from a single Microsoft Kinect V2 sensor using an octree resolution of 4 cm. View Full-Text
Keywords: 3D sensors; point clouds; time-of-flight; lidar; compression; denoising; scalability 3D sensors; point clouds; time-of-flight; lidar; compression; denoising; scalability

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Dybedal, J.; Aalerud, A.; Hovland, G. Embedded Processing and Compression of 3D Sensor Data for Large Scale Industrial Environments. Sensors 2019, 19, 636.

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