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Tapes are essential in surveying for accurate, durable, and short-distance measurements. Made from lightweight, nylon-coated steel, they offer flexibility and strength for rugged outdoor use. The nylon coating protects against rust and wear, extending the tape's life. Standard lengths, around 30 meters, are marked in meters and millimeters for precision.Surveyors select tapes based on site conditions and accuracy needs. Lightweight, nylon-coated tapes are commonly used for ease of handling and...
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Updated: Oct 14, 2025

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Tracking Multiple Vehicles Constrained to a Road Network From a UAV with Sparse Visual Measurements.

Jared J Moore1, Craig C Bidstrup1, Cameron K Peterson1

  • 1Department of Electrical and Computer Engineering, Brigham Young University, Provo, UT, United States.

Frontiers in Robotics and AI
|November 8, 2021
PubMed
Summary

This study introduces a Rao-Blackwellized Particle Filter (RBPF) for unmanned aerial vehicle (UAV) tracking of multiple ground targets on road networks. The RBPF improves target reacquisition and location certainty, even when targets leave and re-enter the UAV's view.

Keywords:
computer visioncooperative controlmultiple target trackingparticle filterunmanned air vehicles

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Multiple-target tracking algorithms struggle with track reallocation when targets move in and out of sensor fields of view, especially for unmanned aerial vehicles (UAVs) tracking ground targets on large road networks.
  • Existing methods face challenges in maintaining target tracks and reallocating them when targets leave and re-enter the UAV's field of view.

Purpose of the Study:

  • To propose a novel Rao-Blackwellized Particle Filter (RBPF) for robust multiple-target tracking on road networks.
  • To enhance probabilistic data association and target track maintenance for UAVs operating beyond their immediate field of view.
  • To improve the certainty of multiple target location estimates using advanced path planning controllers.

Main Methods:

  • Developed a Rao-Blackwellized Particle Filter (RBPF) structured as a particle filter of particle filters for data association and target tracking.
  • Implemented two path planning controllers: receding horizon and deep reinforcement learning, to prioritize paths that minimize target entropy.
  • Incorporated positive and negative information within tracking particle filters upon receiving measurements.

Main Results:

  • The proposed RBPF effectively maintains individual target tracks and performs probabilistic data association for targets constrained to a road network.
  • Path planning controllers demonstrated improved certainty in multiple target location estimates by prioritizing paths that reduce target entropy.
  • An algorithm was developed to compute the upper bound on filter performance, aiding in estimating the number of UAVs required for specific performance thresholds.

Conclusions:

  • The RBPF offers a robust solution for multiple-target tracking in challenging scenarios involving UAVs and road networks.
  • The integration of path planning controllers significantly enhances the accuracy and certainty of target localization.
  • The developed performance bound estimation facilitates resource management and mission planning for multi-UAV tracking systems.