Millimeter Wave Radar From Synthetic Aperture to Probabilistic Mapping

Jul 16, 2026

Uploaded by Oliver

Robust probabilistic mapping underpins autonomous robotics in challenging environments where conventional sensors degrade. Millimeter wave (mmWave) radar can sense through smoke and fog, but accurate probabilistic occupancy mapping remains difficult because radar measurements are inherently sparse and noisy across signal processing steps. The work proposes a full pipeline from raw radar signals to probabilistic occupancy maps, combining Synthetic Aperture Radar processing with probabilistic modeling. Extensive indoor evaluations compare signal-processing and modeling variants, analyze downstream path-planning impact, and study parameter and antenna-array effects. Results quantify both effectiveness and limitations, and an open-source GPU-accelerated dataset and processing pipeline are provided for further research and broader adoption.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages8

Reading Time20 min

AccessFree to download

Introduction

Probabilistic mapping in robotics

Why mmWave radar for adverse conditions

Challenges of radar-based mapping

Adapting SAR for probabilistic occupancy maps

Contributions

Millimeter Wave Radar: From Synthetic Aperture to Probabilistic Mapping Jui-Te Huang, Ruoyang Xu, Michael Kaess arXiv :2607 . 10161v1 [ cs .RO] 11 Jul 2026 Abstract—Robust probabilistic mapping is essential for autonomous robotic systems operating in challenging environments. While traditional sensors fail in adverse conditions such as smoke and fog, millimeter wave (mmWave) radar sensors offer reliable sensing in such conditions. However, creating accurate probabilistic maps from radar data presents significant challenges due to the inherently sparse and noisy characteristics of radio wave measurements and signal processing steps. In an attempt to address these issues, we establish a complete pipeline from raw radar signals to probabilistic occupancy maps, incorporating Synthetic Aperture Radar processing followed by a probabilistic modeling step. We conduct extensive validation across indoor environments, comparing our approach against different signal processing and probabilistic modeling approaches. We also evaluate mapping quality through downstream path planning performance analysis. Furthermore, we investigate the impact of key parameters and antenna array configuration on mapping performance. The experimental results demonstrate both the effectiveness and limitations of SARbased probabilistic mapping for real-world robotic deployment. To facilitate future research and broader adoption, we contribute an open-source cascaded mmWave radar dataset with an accompanying GPU-accelerated signal processing pipeline available at [https://github.com/rpl-cmu/rpm](https://github.com/rpl-cmu/rpm). I. INTRODUCTION Probabilistic mapping is a fundamental building block in the robot autonomy stack, enabling downstream tasks such as exploration, planning, and control. Prior research has demonstrated successful and efficient probabilistic mapping using time-of-flight sensors like LiDARs and depth cameras [1] . However, these sensors experience significant performance degradation in adverse environments such as smoke and fog, where airborne particles obstruct light rays before they reach actual objects. On the other hand, millimeter wave (mmWave) radar sensors transmit and receive electromagnetic waves with millimeter-level wavelengths, providing robust sensing capabilities in adverse environmental conditions without relying on external lighting or heating. Recent developments in nextgeneration mmWave radars utilize novel modulation schemesand multiple antenna packaging to achieve higher spatial resolution. Consequently, radar sensors have attracted significant attention from researchers applying this technology for object detection [2]–[4], navigation [5], state estimation [6]–[10], novel view synthesis [11] and mapping [11]–[18] . Jui-Te Huang and Michael Kaess are, Ruoyang Xu was with School of Computer Science, Robotics Institute, Carnegie Mellon University, PA, USA {juiteh, ruoyangx, [kaess}@andrew.cmu.edu](kaess}@andrew.cmu.edu) This work was partially supported by Amazon Lab126 and the U.S. Army Research Office under Contract No. W519TC230031 . The content of the information does not reflect the position or the policy of the government, and no official endorsement should be inferred. Fig. 1: A demonstration of our proposed method using two cascade mmWave radar boards to create a LiDAR-like occupancy map (bottom right) . As a vehicle moves through the environment, onboard radar sensors create a synthetic aperture to map the surroundings (Top) . A probability modeling method is presented to create the occupancy map (bottom left) . While research on radar-based state estimation is achieving performance comparable to LiDAR and camera-based methods, radar-based mapping remains challenging due to the sparse and noisy nature of processed mmWave radar data. Several studies have demonstrated promising results by training neural networks to generate dense scene geometry under the supervision of LiDAR data [19] . However,

FAQs

Why are probabilistic occupancy maps important for autonomous robots?

They support downstream autonomy tasks such as exploration, planning, and control within the robot stack.

What makes creating probabilistic maps from mmWave radar data challenging?

Processed mmWave radar data is sparse and noisy, and multiple signal processing steps can introduce additional uncertainty.

What does the proposed pipeline do to generate probabilistic occupancy maps?

It converts raw radar signals into probabilistic occupancy maps by applying Synthetic Aperture Radar processing and then performing probabilistic modeling to account for occlusions and object probability distributions.

Jul 16, 2026

Uploaded by Oliver