Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

A robust baro-radar-inertial odometry m-estimator for multicopter navigation in cities and forests

Rik Girod, Marco Hauswirth, Patrick Pfreundschuh, Mariano Biasio, Roland Siegwart

TL;DR

This work presents BRIO, a robust baro-radar-inertial odometry framework for multicopters operating in GNSS-denied urban and forest environments. It formulates a factor-graph MAP estimator with robust bearing Doppler and differential barometer factors to fuse bearing Doppler radar detections with IMU data, achieving stable, low-drift trajectories in the presence of moving objects and multipath. The key contributions are the BRIO m-estimator, a robust differential barometry approach, and zero-velocity tracking, all implemented in an open-source pipeline and validated on real flights and the ColoRadar dataset. The results demonstrate drift as low as a few tenths of a percent per distance traveled and strong generalization across environments, highlighting the practicality of radar-based navigation as a robust alternative to vision or LiDAR in GNSS-denied operations.

Abstract

Search and rescue operations require mobile robots to navigate unstructured indoor and outdoor environments. In particular, actively stabilized multirotor drones need precise movement data to balance and avoid obstacles. Combining radial velocities from on-chip radar with MEMS inertial sensing has proven to provide robust, lightweight, and consistent state estimation, even in visually or geometrically degraded environments. Statistical tests robustify these estimators against radar outliers. However, available work with binary outlier filters lacks adaptability to various hardware setups and environments. Other work has predominantly been tested in handheld static environments or automotive contexts. This work introduces a robust baro-radar-inertial odometry (BRIO) m-estimator for quadcopter flights in typical GNSS-denied scenarios. Extensive real-world closed-loop flights in cities and forests demonstrate robustness to moving objects and ghost targets, maintaining a consistent performance with 0.5 % to 3.2 % drift per distance traveled. Benchmarks on public datasets validate the system's generalizability. The code, dataset, and video are available at https://github.com/ethz-asl/rio.

A robust baro-radar-inertial odometry m-estimator for multicopter navigation in cities and forests

TL;DR

This work presents BRIO, a robust baro-radar-inertial odometry framework for multicopters operating in GNSS-denied urban and forest environments. It formulates a factor-graph MAP estimator with robust bearing Doppler and differential barometer factors to fuse bearing Doppler radar detections with IMU data, achieving stable, low-drift trajectories in the presence of moving objects and multipath. The key contributions are the BRIO m-estimator, a robust differential barometry approach, and zero-velocity tracking, all implemented in an open-source pipeline and validated on real flights and the ColoRadar dataset. The results demonstrate drift as low as a few tenths of a percent per distance traveled and strong generalization across environments, highlighting the practicality of radar-based navigation as a robust alternative to vision or LiDAR in GNSS-denied operations.

Abstract

Search and rescue operations require mobile robots to navigate unstructured indoor and outdoor environments. In particular, actively stabilized multirotor drones need precise movement data to balance and avoid obstacles. Combining radial velocities from on-chip radar with MEMS inertial sensing has proven to provide robust, lightweight, and consistent state estimation, even in visually or geometrically degraded environments. Statistical tests robustify these estimators against radar outliers. However, available work with binary outlier filters lacks adaptability to various hardware setups and environments. Other work has predominantly been tested in handheld static environments or automotive contexts. This work introduces a robust baro-radar-inertial odometry (BRIO) m-estimator for quadcopter flights in typical GNSS-denied scenarios. Extensive real-world closed-loop flights in cities and forests demonstrate robustness to moving objects and ghost targets, maintaining a consistent performance with 0.5 % to 3.2 % drift per distance traveled. Benchmarks on public datasets validate the system's generalizability. The code, dataset, and video are available at https://github.com/ethz-asl/rio.
Paper Structure (19 sections, 16 equations, 9 figures, 2 tables)

This paper contains 19 sections, 16 equations, 9 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Notation
  4. Radar-inertial State Estimation
  5. System State
  6. Measurements
  7. Optimization Criterion
  8. Robust bearing Doppler Factor
  9. Robust Barometric Factor
  10. Zero-Velocity Detection Tracking
  11. IMU Factor
  12. Prior Factor
  13. Experiments
  14. Exceptional Robustness
  15. Performance Overview
  16. ...and 4 more sections

Figures (9)

  • Figure 1: Onboard view from our radar-stabilized quadcopter. The points show 1s accumulated, overlayed radar detections colored by their radial velocity (green: zero, orange: towards the radar, blue: away from the radar).
  • Figure 2: Quadcopter side view with coordinate frames and two radar detections.
  • Figure 3: Factor graph representation with two, one, and four Doppler measurements and a zero-velocity landmark observed from the first and second state.
  • Figure 4: Robust bearing Doppler loss function analysis based on streetcar passing hovering quadcopter. Radar detections accumulated over 10s optimization window.
  • Figure 5: Barometer noise and robust loss analysis.
  • ...and 4 more figures