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Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...
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GPS surveying methods vary in application, accuracy, and data collection techniques, catering to diverse surveying and mapping needs. Static GPS, kinematic GPS, and real-time kinematic (RTK) surveying are widely used. Each technique offers distinct advantages.Static GPS involves placing one receiver at a known reference point and another at the target point. It collects exact positional data by observing multiple satellite ranges over an extended period, achieving centimeter-level accuracy for...
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The Global Positioning System (GPS) has become an indispensable tool in fieldwork, offering unparalleled precision and efficiency for surveying, navigation, and infrastructure development. By harnessing signals from a constellation of satellites, GPS receivers determine the location of objects with remarkable speed and accuracy, often completing calculations within a second.Advantages of Modern GPS TechnologyContemporary GPS receivers are designed to meet the practical demands of field...
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The Global Positioning System (GPS) revolutionized positioning on Earth, providing precise location data through satellite ranging. The GPS system was developed in 1978 by the U.S. Department of Defense  for military use, and it became available for civilian applications in 1983, transforming fields including navigation, fleet management, and time synchronization for telecommunications systems.GPS consists of satellites in medium Earth orbit, about 20,200 kilometers above the surface,...
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Global Positioning System (GPS) technology has revolutionized navigation and positioning, but its accuracy is often compromised by various errors. These errors, stemming from environmental, satellite, and receiver-related factors, require careful mitigation to ensure reliable performance across applications.Atmospheric ErrorsGPS signals travel through the Earth’s ionosphere and troposphere, introducing delays which affect accuracy. The ionosphere is strongly influenced by charged particles,...
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Related Experiment Video

Updated: Jul 30, 2025

A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software
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Ackerman Unmanned Mobile Vehicle Based on Heterogeneous Sensor in Navigation Control Application.

Chi-Huang Shih1, Cheng-Jian Lin1, Jyun-Yu Jhang2

  • 1Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung 411, Taiwan.

Sensors (Basel, Switzerland)
|May 13, 2023
PubMed
Summary

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.