NOTICE: Due to a lapse in annual appropriations, most of this website is not being updated. Learn more.

Form submissions will still be accepted but will not receive responses at this time. Sections of this site for programs using non-appropriated funds (such as NVLAP) or those that are excepted from the shutdown (such as CHIPS and NVD) will continue to be updated.

U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

PUBLICATIONS

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

Published

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

Download Paper

Local Download

Keywords

antennas, electormagnetics, propagation, measurements
Wireless (RF), Virtual / augmented reality, Next generation networks, Networking, Network test and measurement, Network modeling and analysis, Mobile and wireless networking, Mobile, Machine learning, Internet of Things (IoT), Hardware for AI, Artificial intelligence, Applied AI, AI measurement and evaluation and Advanced communications

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 21, 2025)

Issues

If you have any questions about this publication or are having problems accessing it, please contact [email protected].

Created April 26, 2024, Updated July 14, 2025
Was this page helpful?