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Doppler-only Single-scan 3D Vehicle Odometry

Andres Galeote-Luque, Vladimír Kubelka, Martin Magnusson, Jose-Raul Ruiz-Sarmiento, Javier Gonzalez-Jimenez

TL;DR

A novel 3D odometry method that recovers the full motion of a vehicle only from a Doppler-capable range sensor, which provides a more reliable translation of the sensor, compared to the errors linked to IMUs due to noise and biases.

Abstract

We present a novel 3D odometry method that recovers the full motion of a vehicle only from a Doppler-capable range sensor. It leverages the radial velocities measured from the scene, estimating the sensor's velocity from a single scan. The vehicle's 3D motion, defined by its linear and angular velocities, is calculated taking into consideration its kinematic model which provides a constraint between the velocity measured at the sensor frame and the vehicle frame. Experiments carried out prove the viability of our single-sensor method compared to mounting an additional IMU. Our method provides the translation of the sensor, which cannot be reliably determined from an IMU, as well as its rotation. Its short-term accuracy and fast operation (~5ms) make it a proper candidate to supply the initialization to more complex localization algorithms or mapping pipelines. Not only does it reduce the error of the mapper, but it does so at a comparable level of accuracy as an IMU would. All without the need to mount and calibrate an extra sensor on the vehicle.

Doppler-only Single-scan 3D Vehicle Odometry

TL;DR

A novel 3D odometry method that recovers the full motion of a vehicle only from a Doppler-capable range sensor, which provides a more reliable translation of the sensor, compared to the errors linked to IMUs due to noise and biases.

Abstract

We present a novel 3D odometry method that recovers the full motion of a vehicle only from a Doppler-capable range sensor. It leverages the radial velocities measured from the scene, estimating the sensor's velocity from a single scan. The vehicle's 3D motion, defined by its linear and angular velocities, is calculated taking into consideration its kinematic model which provides a constraint between the velocity measured at the sensor frame and the vehicle frame. Experiments carried out prove the viability of our single-sensor method compared to mounting an additional IMU. Our method provides the translation of the sensor, which cannot be reliably determined from an IMU, as well as its rotation. Its short-term accuracy and fast operation (~5ms) make it a proper candidate to supply the initialization to more complex localization algorithms or mapping pipelines. Not only does it reduce the error of the mapper, but it does so at a comparable level of accuracy as an IMU would. All without the need to mount and calibrate an extra sensor on the vehicle.
Paper Structure (11 sections, 9 equations, 7 figures)

This paper contains 11 sections, 9 equations, 7 figures.

Table of Contents

  1. INTRODUCTION
  2. RELATED WORK
  3. METHOD OVERVIEW
  4. SENSOR VELOCITY
  5. VEHICLE KINEMATIC MODEL
  6. COVARIANCE ANALYSIS
  7. EXPERIMENTS AND RESULTS
  8. SENSOR CALIBRATION
  9. ACCURACY RESULTS
  10. INITIALIZATION OF SLAM
  11. CONCLUSIONS

Figures (7)

  • Figure 1: Representation of the working principle of the proposed method. Top, the radial velocity $\vec{v}_i^D$ of the observed points in the scene is leveraged to estimate the sensor's linear velocity $\vec{v}_s$. Bottom, the kinematic model provides the relation between $\vec{v}_s$ and both the linear $\vec{v}$ and angular $\vec{\omega}$ velocities of the vehicle.
  • Figure 2: Simple cases of movement of the vehicle showing the location of the line along which the ICR can be found (orange). The velocity of points located on the ICR axis is perpendicular to it.
  • Figure 3: Sensor setup and test environment from sequence 04.
  • Figure 4: Angular velocity estimated from the Doppler velocities compared to IMU measurements, from sequence 11.
  • Figure 5: Translational and rotational RPE per frame.
  • ...and 2 more figures