Skip to content

RadarML:
A modular ecosystem for learning on radar spectrum

What is RadarML?

Radar, in many ways, is an ideal complement to cameras for 3D perception. As a low cost1, solid-state sensor, radar provides low range ambiguity2 — as opposed to the low angular ambiguity of cameras — as well as the ability to work in the dark, through occlusions such as fog, rain, and mud3, and directly measure velocity.

Radar Camera Lidar
Cost $ $ $$$
Angular Ambiguity High Low Low
Range Ambiguity Low High Low

However, unlike cameras and lidar, radar data are difficult to collect and unintuitive to interpret. Especially in the case of raw I/Q data, few tools and datasets exist, creating a high barrier to entry for research. Our goal is to fill this gap by providing high-quality, modular, and fully open-source data collection tools, processing pipelines, datasets, and research frameworks to enable learning on radar spectrum.

Active Projects

  • nrdk

    MIT License GitHub Supports Python 3.12+

    neural radar development kit for deep learning on multimodal radar data

  • red-rover

    GitHub GitHub Supports Python 3.10+

    a system for collecting and processing mmWave Radar, camera, and Lidar data

  • xwr

    GitHub pypi version PyPI - Python Version

    python interface for collecting raw time signal data from TI mmWave radars

  • abstract_dataloader

    GitHub pypi version PyPI - Python Version

    abstract interface for composable dataloaders and preprocessing pipelines

Upcoming Projects

Pre-Release

Get in touch if you're interested in contributing or joining our closed alpha!

Contact: Tianshu Huang (tianshu2@andrew.cmu.edu)

  • i/q-1m

    CC BY License Target Release Date: Q4 2025 Roverd Data Format

    29 hours of radar (time signal), lidar, and camera across indoor, outdoor, and bike-mounted settings

  • mmwcas

    License TBD Early Development

    data collection and processing for the TI MMWCAS cascaded imaging radar

  • xwr-ros

    MIT License Early Development

    ROS node and type for XWR radar data

  1. Single-chip mmWave radars typically cost $10-50, compared to $1000+ for lidar. ↩

  2. Radars provide constant range resolution regardless of distance, while cameras can only measure depth via triangulation (either as a stereo pair or via structure-from-motion), which degrades with distance (relative to baseline). ↩

  3. Unlike cameras and Lidars, mmWave radars can see through sensor occlusions such as mud or paint, and are robust to atmospheric occlusion such as fog and rain. By exploiting multipath, radars can also see around objects to some extent. ↩