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Ackerman Unmanned Mobile Vehicle Based on Heterogeneous Sensor in Navigation Control Application.

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This study enhances unmanned mobile vehicle (UMV) environmental perception by fusing camera and LiDAR data. This sensor fusion improves object detection and navigation accuracy for autonomous driving applications.

Keywords:
Ackerman unmanned mobile vehicledeep learningheterogeneous sensornavigation controlobject detection

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Area of Science:

  • Robotics and Autonomous Systems
  • Sensor Fusion
  • Computer Vision

Background:

  • Unmanned Mobile Vehicles (UMVs) are critical in industry, requiring advanced navigation and environmental recognition.
  • Single sensors in UMVs face limitations due to environmental factors, impacting identification accuracy.
  • Heterogeneous sensor fusion is key to overcoming individual sensor limitations for robust UMV performance.

Purpose of the Study:

  • To enhance environmental detection and identification accuracy and stability in an Ackerman UMV.
  • To implement a fusion technique combining imaging and LiDAR sensors for improved UMV perception.
  • To enable reliable navigation and obstacle avoidance for UMVs in dynamic environments.

Main Methods:

  • Utilized a camera for real-time imaging, employing YOLOv4-tiny and Simple Online Real-time Tracking for object detection, classification, and tracking.
  • Integrated LiDAR for real-time distance measurements of detected objects.
  • Employed an Inertial Measurement Unit for odometry and Simultaneous Localization and Mapping (SLAM) for static map creation.

Main Results:

  • The proposed fusion technique successfully integrated data from heterogeneous sensors (camera, LiDAR, IMU).
  • The Ackerman UMV demonstrated the ability to navigate to a target point while simultaneously identifying obstacles and pedestrians.
  • Enhanced accuracy and stability in environmental detection and identification were achieved through sensor fusion.

Conclusions:

  • Fusion technology employing heterogeneous sensors significantly improves UMV environmental perception capabilities.
  • The developed system enables reliable navigation and real-time obstacle/pedestrian identification for UMVs.
  • This approach provides a robust solution for autonomous driving and environmental recognition in UMVs.