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

Jui-Te Huang, Ruoyang Xu, Akshay Hinduja, Michael Kaess

TL;DR

A strategy that seamlessly integrates radar data with a consumer-grade IMU sensor using fixed-lag smoothing optimization is introduced and a method to optimize body frame velocity while managing Doppler velocity uncertainty is presented.

Abstract

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.

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

TL;DR

A strategy that seamlessly integrates radar data with a consumer-grade IMU sensor using fixed-lag smoothing optimization is introduced and a method to optimize body frame velocity while managing Doppler velocity uncertainty is presented.

Abstract

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.
Paper Structure (16 sections, 12 equations, 5 figures, 1 table)

This paper contains 16 sections, 12 equations, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Radar-Inertial Odometry
  4. Multi-Radar for State Estimation
  5. Methodology
  6. Velocity Estimation using Radar Doppler Velocity
  7. Preintegrated IMU Factor
  8. Body Frame Velocity Factor
  9. IMU Static Initialization
  10. Factor Graph Formulation
  11. Experiments
  12. Dual Cascade Imaging Radar Dataset
  13. Doppler and Velocity Uncertainty Evaluation
  14. Quantitative Evaluation
  15. Qualitative Evaluation
  16. ...and 1 more sections

Figures (5)

  • Figure 1: A demonstration of multi-radar inertial odometry (orange) walked through different levels of an atrium compared to visual-inertial odometry (green). The colors of the radar point cloud indicate Doppler velocity from high (red) to low (purple).
  • Figure 2: An illustration of our multi-radar inertial state estimation system in the form of a factor graph. The body frame velocity factor can be built with either our horizontal or vertical imaging radar.
  • Figure 3: The probability density of the Doppler velocity error distribution is calculated by comparing the velocities of 1,092,224 radar points with the VIO body-frame velocity projected in their respective directions.
  • Figure 4: Standard deviation on XYZ axes of the estimated body frame velocity from two radars.
  • Figure 5: Comparing the trajectories of visual-inertial odometry to radar-inertial odometry using either single horizontal/vertical radar or dual radars. Each column represents our 3D motion sequences, with the top row displaying the top-down view and the bottom row presenting the side view.