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Pronto: A Multi-Sensor State Estimator for Legged Robots in Real-World Scenarios.

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Summary

This study introduces Pronto, a flexible state estimation framework for legged robots. Pronto uses an Extended Kalman Filter (EKF) to fuse sensor data, improving robot navigation in challenging real-world conditions.

Keywords:
extended kalman filter (EKF)iterative closest point (ICP)legged robotssensor fusionstate estimationvisual odometry

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Area of Science:

  • Robotics
  • State Estimation
  • Sensor Fusion

Background:

  • Real-world legged robot operation faces challenges like occlusions, low light, and rough terrain, impairing state estimation.
  • Accurate pose and velocity estimation are critical for legged robot control and navigation.

Purpose of the Study:

  • To present Pronto, a modular and flexible state estimation framework for legged robots.
  • To enable robust robot operation in challenging real-world environments.
  • To provide open-source algorithms for the research community.

Main Methods:

  • Utilizes an Extended Kalman Filter (EKF) to fuse Inertial Measurement Unit (IMU) and Leg Odometry data.
  • Integrates pose corrections from visual and LIDAR odometry in a loosely coupled manner.
  • Maintains a high-frequency proprioceptive estimation thread (250-1,000 Hz) and incorporates low-frequency exteroceptive updates (1-15 Hz).

Main Results:

  • Demonstrated robustness and versatility across humanoid (Atlas, Valkyrie) and quadruped (HyQ, ANYmal) robots.
  • Achieved over 2 hours of total runtime and 1.37 km of distance traveled in diverse field scenarios.
  • Successfully handled challenging conditions including occlusions, low light, rough terrain, and dynamic obstacles.

Conclusions:

  • Pronto provides a robust and adaptable solution for legged robot state estimation in real-world conditions.
  • The framework's ability to fuse multiple sensor modalities enhances navigation performance.
  • Open-sourcing the algorithms promotes further research and development in legged robotics.