ars548_ros: An ARS 548 RDI radar driver for ROS

The ARS 548 RDI Radar is a premium model of the fifth generation of 77 GHz long-range radar sensors with new RF antenna arrays, which offer digital beamforming. This radar measures independently the distance, speed, and angle of objects without any reflectors in one measurement cycle based on Pulse Compression with New Frequency Modulation. Unfortunately, to the best of our knowledge, there are no open-source drivers available for Linux systems to enable users to analyze the data acquired by the sensor. In this paper, we present a driver that can interpret the data from the ARS 548 RDI sensor and make it available over the Robot Operating System versions 1 and 2 (ROS and ROS2). Thus, these data can be stored, represented, and analyzed using the powerful tools offered by ROS. Besides, our driver offers advanced object features provided by the sensor, such as relative estimated velocity and acceleration of each object, its orientation and angular velocity. We focus on the configuration of the sensor and the use of our driver including its filtering and representation tools. Besides, we offer a video tutorial to help in its configuration process. Finally, a dataset acquired with this sensor and an Ouster OS1-32 LiDAR sensor, to have baseline measurements, is available so that the user can check the correctness of our driver.

Authors

Institutions

Publication Details

Journal
SoftwareX
Published
2025-03-06
DOI
https://doi.org/10.1016/j.softx.2025.102111
Citations
2
Primary Topic
Robotics and Sensor-Based Localization
Type
article
Field-Weighted Citation Impact
9.99

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

ars548_ros: An ARS 548 RDI radar driver for ROS

Fernando Caballero, Luís Merino, David Alejo, Fernando Fernández-Calatayud et al.
2 citations
SoftwareX
Robotics and Sensor-Based Localization
9.99
article

ars548_ros: An ARS 548 RDI radar driver for ROS

Fernando Caballero, Luís Merino, David Alejo, Fernando Fernández-Calatayud, José Javier Carpio, Lucía Coto
article en
2 citations

Abstract

The ARS 548 RDI Radar is a premium model of the fifth generation of 77 GHz long-range radar sensors with new RF antenna arrays, which offer digital beamforming. This radar measures independently the distance, speed, and angle of objects without any reflectors in one measurement cycle based on Pulse Compression with New Frequency Modulation. Unfortunately, to the best of our knowledge, there are no open-source drivers available for Linux systems to enable users to analyze the data acquired by the sensor. In this paper, we present a driver that can interpret the data from the ARS 548 RDI sensor and make it available over the Robot Operating System versions 1 and 2 (ROS and ROS2). Thus, these data can be stored, represented, and analyzed using the powerful tools offered by ROS. Besides, our driver offers advanced object features provided by the sensor, such as relative estimated velocity and acceleration of each object, its orientation and angular velocity. We focus on the configuration of the sensor and the use of our driver including its filtering and representation tools. Besides, we offer a video tutorial to help in its configuration process. Finally, a dataset acquired with this sensor and an Ouster OS1-32 LiDAR sensor, to have baseline measurements, is available so that the user can check the correctness of our driver.

SoftwareXVol. 30
Universidad de Sevilla (ES), Universidad Pablo de Olavide (ES)
Ministerio de Ciencia, Innovación y Universidades, Ministerio de Ciencia e Innovación
Openalex Percentile: Top 3%
Robotics and Sensor-Based Localization
9.99
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.