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Fail-Aware LIDAR-Based Odometry for Autonomous Vehicles.

Iván García Daza1, Mónica Rentero1, Carlota Salinas Maldonado1

  • 1Computer Engineering Department, Universidad de Alcalá, 28805 Alcalá de Henares, Spain.

Sensors (Basel, Switzerland)
|July 29, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a fail-aware LiDAR odometry system for autonomous vehicles, enhancing safety by enabling autonomous emergency stops. The system minimizes drift error for reliable localization, crucial for user acceptance.

Keywords:
LiDAR odometryautomated drivingfail-awarefail-operational systems

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Area of Science:

  • Robotics
  • Computer Vision
  • Autonomous Systems

Background:

  • Autonomous driving systems require high user acceptance, but current architectures need driver intervention during failures.
  • Driver state issues like fatigue and distraction pose risks during manual takeovers.

Purpose of the Study:

  • To present a redundant, accurate, robust, and scalable LiDAR odometry system with fail-aware capabilities.
  • To enable safe autonomous stop maneuvers without driver mediation.
  • To address the drift error inherent in odometry systems for long-term localization.

Main Methods:

  • Developed a LiDAR odometry system integrating Iterative Closest Points (ICP), environment feature extraction, and Singular Value Decomposition (SVD).
  • Implemented a dynamic 6-DoF model to minimize odometry error.
  • Introduced a fail-aware indicator to estimate reliable localization time windows.

Main Results:

  • Achieved 1.00% translation and 0.0041 deg/m rotation error on the KITTI dataset (12th among LiDAR-only methods).
  • The fail-aware indicator demonstrated the system's safety and reliability.
  • Validated the necessity of complex models and sensor fusion for accurate odometry.

Conclusions:

  • Accurate LiDAR odometry requires advanced models and sensor fusion techniques.
  • Integrating a fail-aware indicator is essential for safe, redundant localization in autonomous systems.
  • The proposed system enhances the safety and reliability of autonomous driving.