# A New Wave in Robotics: Survey on Recent mmWave Radar Applications in Robotics

arXiv: https://arxiv.org/abs/2305.01135 · HTML: https://mlsanity.com/paper/2305.01135/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2023.

## Built on by (most cited first, 18 of 18)

- [RadarSplat: Radar Gaussian Splatting for High-Fidelity Data Synthesis and 3D Reconstruction of Autonomous Driving Scenes](https://mlsanity.com/paper/2506.01379/) (2025)
- [TacoDepth: Towards Efficient Radar-Camera Depth Estimation with One-stage Fusion](https://mlsanity.com/paper/2504.11773/) (2025)
- [Ultra-High-Frequency Harmony: mmWave Radar and Event Camera Orchestrate Accurate Drone Landing](https://mlsanity.com/paper/2502.14992/) (2025)
- [M4Human: A Large-Scale Multimodal mmWave Radar Benchmark for Human Mesh Reconstruction](https://mlsanity.com/paper/2512.12378/) (2025)
- [HeRCULES: Heterogeneous Radar Dataset in Complex Urban Environment for Multi-session Radar SLAM](https://mlsanity.com/paper/2502.01946/) (2025)
- [MOANA: Multi-Radar Dataset for Maritime Odometry and Autonomous Navigation Application](https://mlsanity.com/paper/2412.03887/) (2024)
- [ThermRad: A Multi-modal Dataset for Robust 3D Object Detection under Challenging Conditions](https://mlsanity.com/paper/2308.10161/) (2023)
- [Pointing the Way: Refining Radar-Lidar Localization Using Learned ICP Weights](https://mlsanity.com/paper/2309.08731/) (2023)
- [Simulating Automotive Radar with Lidar and Camera Inputs](https://mlsanity.com/paper/2503.08068/) (2025)
- [ELMAR: Enhancing LiDAR Detection with 4D Radar Motion Awareness and Cross-modal Uncertainty](https://mlsanity.com/paper/2506.17958/) (2025)
- [Radar-Camera Fused Multi-Object Tracking: Online Calibration and Common Feature](https://mlsanity.com/paper/2510.20794/) (2025)
- [RaUF: Learning the Spatial Uncertainty Field of Radar](https://mlsanity.com/paper/2603.01026/) (2026)
- [Dr-BA: Separable Optimization for Direct Radar Bundle Adjustment & Localization](https://mlsanity.com/paper/2605.07041/) (2026)
- [Sensor-Conditioned Representation Learning via Scene-Relevant Observation Quotients](https://mlsanity.com/paper/2606.16210/) (2026)
- [IRGNN: Efficient Invariant Radar Graph Neural Network for Radar Point Cloud Object Detection](https://mlsanity.com/paper/2608.14394/) (2026)
- [3D Point Splatting for mmWave Radar Novel View Synthesis](https://mlsanity.com/paper/2609.11894/) (2026)
- [Do Spinning Radar Doppler Velocity Measurements Improve Vehicle Detection and Tracking?](https://mlsanity.com/paper/2609.21000/) (2026)
- [DiFF: Doppler-informed Flow Matching for Human Motion Flow](https://mlsanity.com/paper/2609.39098/) (2026)

## Abstract (arXiv)

We survey the current state of millimeterwave (mmWave) radar applications in robotics with a focus on unique capabilities, and discuss future opportunities based on the state of the art. Frequency Modulated Continuous Wave (FMCW) mmWave radars operating in the 76--81GHz range are an appealing alternative to lidars, cameras and other sensors operating in the near visual spectrum. Radar has been made more widely available in new packaging classes, more convenient for robotics and its longer wavelengths have the ability to bypass visual clutter such as fog, dust, and smoke. We begin by covering radar principles as they relate to robotics. We then review the relevant new research across a broad spectrum of robotics applications beginning with motion estimation, localization, and mapping. We then cover object detection and classification, and then close with an analysis of current datasets and calibration techniques that provide entry points into radar research.