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Related Experiment Videos

A Pose Initialization Method for Unmanned Vehicles Based on an Improved Siamese Neural Network and Multi-Stage

Jian Yang1, Biao Chen1, Weiye Shen1

  • 1College of Mechanical Engineering, Yangzhou University, Yangzhou 225127, China.

Sensors (Basel, Switzerland)
|June 12, 2026
PubMed
Summary

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Vector Functions and Motion: Problem Solving01:30

Vector Functions and Motion: Problem Solving

Accurate position tracking is fundamental to the safe and effective operation of unmanned aerial vehicles (UAVs), particularly during precision maneuvers near complex structures. In this scenario, a drone is programmed to perform a high-precision inspection of a vertical structure, starting at position ((x, y, z) = (3, 0, 0)), with an initial velocity oriented in the positive z-direction. The trajectory of the drone is governed by a time-dependent acceleration function a(t), which is predefined...

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This summary is machine-generated.

This study introduces a novel pose initialization framework for satellite-denied environments. It uses a Siamese Neural Network (SNN) and multi-stage registration to achieve centimeter-level accuracy for autonomous systems.

Area of Science:

  • Robotics and Autonomous Systems
  • Computer Vision
  • Sensor Fusion

Background:

  • Conventional localization methods fail in satellite-denied environments due to lack of GPS.
  • Rapid and precise pose initialization is critical for autonomous navigation in GPS-denied areas.

Purpose of the Study:

  • To develop a high-precision pose initialization framework for satellite-denied environments.
  • To enable accurate localization using radar point clouds and prior maps.

Main Methods:

  • A Siamese Neural Network (SNN) with CBAM for feature matching between radar point clouds and map slices.
  • Adaptive Monte Carlo Localization (AMCL) for probabilistic slice identification and initial pose refinement.
  • Normal Distributions Transform (NDT) for centimeter-level pose estimation.
Keywords:
Siamese neural networkadaptive Monte Carlo localizationlocalizationnormal distributions transform

Related Experiment Videos

Main Results:

  • The SNN successfully identifies correct map slices, with AMCL and NDT refining pose accuracy to centimeter-level.
  • Achieved 99% localization success rates with low RMSEs for distance and orientation on various map scales.
  • Demonstrated robust initialization performance on the KITTI dataset in complex outdoor environments.

Conclusions:

  • The proposed multi-stage framework offers a reliable solution for high-precision pose initialization in large-scale satellite-denied scenarios.
  • The integration of SNN, AMCL, and NDT significantly enhances localization accuracy and robustness.