ARS548_ros. An ARS 548 RDI radar driver for ROS

TL;DR

The paper presents ars548_ros, an open-source ROS and ROS2 driver for the ARS548 RDI radar that enables access to rich radar data in standard and custom ROS messages. The architecture comprises two ROS packages (driver and message definitions) with a filtering module and a radar configuration utility, implemented over UDP, and tested on an ARCO platform alongside an OS1-32 LiDAR baseline. Key contributions include real-time data translation to PointCloud2 and PoseArray, a flexible object-filtering mechanism, and flexible time-stamping and visualization support, plus field experiments for traffic monitoring and SLAM validation. The work advances robust radar-enabled perception in degraded-visibility environments and supports subsequent radar odometry and LiDAR-radar fusion research, with dataset and tutorials to facilitate adoption.

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 beam forming. 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.

Figures (6)

• Figure 1: Continental ARS 548 RDI high resolution radar sensor.
• Figure 2: a) Basic flow diagram of ARS 548 driver. b) Example application of the filter: detection of objects moving faster than a threshold velocity.
• Figure 3: Radar detection measurements captured by the ARS 548 RDI sensor in blue and OS1 LiDAR measurements in red. Please note the alignment between LiDAR and radar measurements.
• Figure 4: Our ARCO robotic platform equipped with both an ARS 548 RDI sensor and an OS1-32 LiDAR sensor. The necessary equipment for the experiments is pointed out on the Figure.
• Figure 5: Snapshot of Experiment 1. (left) Radar detections in blue, radar objects in red, radar object moving faster than 10 km/h in a green sphere and LiDAR measurements in white. (right) Geolocalized radar measurements represented over a map from OpenStreetMap OpenStreetMap. Red lines represent cumulative radar detections of two different vehicles over the map of the environment.