# Multi-Radar Inertial Odometry for 3D State Estimation using mmWave Imaging Radar

arXiv: https://arxiv.org/abs/2311.08608 · HTML: https://mlsanity.com/paper/2311.08608/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2023.

## Built on by (most cited first, 5 of 5)

- [Towards Dense and Accurate Radar Perception Via Efficient Cross-Modal Diffusion Model](https://mlsanity.com/paper/2403.08460/) (2024)
- [R2LDM: An Efficient 4D Radar Super-Resolution Framework Leveraging Diffusion Model](https://mlsanity.com/paper/2503.17097/) (2025)
- [mmDEAR: mmWave Point Cloud Density Enhancement for Accurate Human Body Reconstruction](https://mlsanity.com/paper/2503.02375/) (2025)
- [RadarSplat-RIO: Indoor Radar-Inertial Odometry with Gaussian Splatting-Based Radar Bundle Adjustment](https://mlsanity.com/paper/2604.13492/) (2026)
- [The Coastline as a Structural Constraint: Harnessing Scene Geometry for Autonomous Surface Vessel Localization](https://mlsanity.com/paper/2608.21276/) (2026)

## Abstract (arXiv)

State estimation is a crucial component for the successful implementation of robotic systems, relying on sensors such as cameras, LiDAR, and IMUs. However, in real-world scenarios, the performance of these sensors is degraded by challenging environments, e.g. adverse weather conditions and low-light scenarios. The emerging 4D imaging radar technology is capable of providing robust perception in adverse conditions. Despite its potential, challenges remain for indoor settings where noisy radar data does not present clear geometric features. Moreover, disparities in radar data resolution and field of view (FOV) can lead to inaccurate measurements. While prior research has explored radar-inertial odometry based on Doppler velocity information, challenges remain for the estimation of 3D motion because of the discrepancy in the FOV and resolution of the radar sensor. In this paper, we address Doppler velocity measurement uncertainties. We present a method to optimize body frame velocity while managing Doppler velocity uncertainty. Based on our observations, we propose a dual imaging radar configuration to mitigate the challenge of discrepancy in radar data. To attain high-precision 3D state estimation, we introduce a strategy that seamlessly integrates radar data with a consumer-grade IMU sensor using fixed-lag smoothing optimization. Finally, we evaluate our approach using real-world 3D motion data.