AI-Based Environment Segmentation Using a Context-Aware Channel Sounder

Published

April 26, 2024

Author(s)

Anuraag Bodi, Samuel Berweger, Raied Caromi, Jihoon Bang, Jelena Senic, Camillo Gentile

Abstract

We describe how the data acquired from the camera and Lidar systems of our context-aware radio-frequency (RF) channel sounder is used to reconstruct a 3D mesh of the surrounding environment, segmented and classified into discrete objects. First, the images captured by the camera are segmented into objects through an AI-based algorithm. Then the segmented images are projected onto the point cloud captured by the Lidar. Since the receiver end of the channel sounder is mounted on a mobile robot, the data is acquired in the local coordinate system and so must be transformed to a global coordinate system to synthesize a single, holistic point cloud of the environment. Finally, the synthesized point cloud is tessellated into a 3D mesh. The segmented mesh can be used for the automated - i.e., without human analysis - reduction of the data acquired by the RF system of the sounder into an object-specific channel model.

Proceedings Title

18th European Conference on Antennas and Propagation

Conference Dates

March 17-22, 2024

Conference Location

Glasgow, GB

Pub Type

Conferences

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Keywords

antennas, electormagnetics, propagation, measurements

Citation

Bodi, A. , Berweger, S. , Caromi, R. , Bang, J. , Senic, J. and Gentile, C. (2024), AI-Based Environment Segmentation Using a Context-Aware Channel Sounder, 18th European Conference on Antennas and Propagation, Glasgow, GB, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=956786 (Accessed October 1, 2025)