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Characterizing Pedestrian Network from Segmented 3D Point Clouds for Accessibility Assessment: A Virtual Robotic

Ali Ahmadi1, Mir Abolfazl Mostafavi1,2, Ernesto Morales2,3

  • 1Center for Research in Geospatial Data and Intelligence (CRDIG), Department of Geomatics Sciences, Université Laval, 1055, Avenue du Séminaire, Quebec City, QC G1V 0A6, Canada.

Sensors (Basel, Switzerland)
|April 14, 2026
PubMed
Summary
This summary is machine-generated.

A new virtual robot method automatically assesses pedestrian accessibility using 3D data. This approach accurately measures path features, offering personalized assessments for Canadians with disabilities.

Keywords:
3D point cloudTangent Bugaccessibility assessmentcharacterizationenvironmental factorsrobotic

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Area of Science:

  • Robotics and Artificial Intelligence
  • Geospatial Data Analysis
  • Accessibility Research

Background:

  • Millions of Canadians with disabilities require scalable methods for assessing pedestrian network accessibility.
  • Current manual assessment methods are time-consuming, costly, and lack comprehensive data capture.
  • Bridging the gap between building entrances and public walkways is crucial for inclusive urban design.

Purpose of the Study:

  • To introduce a novel virtual robotic system for automated characterization of pedestrian network accessibility.
  • To extend the Tangent Bug navigation algorithm to 3D point cloud environments for enhanced robotic mobility.
  • To enable personalized accessibility assessments by collecting continuous raw measurements of environmental features.

Main Methods:

  • Utilizing semantically segmented 3D LiDAR point clouds as input data.
  • Employing a triangular virtual robot integrated with an extended Tangent Bug navigation algorithm.
  • Extracting key accessibility parameters such as running slope, cross-slope, path width, surface type, and step height, aligned with the Measure of Environmental Accessibility (MEA) framework.

Main Results:

  • Achieved high accuracy in path width measurement (NRMSE = 2.71%) and reliable slope tracking.
  • Successfully characterized both formal sidewalk segments and transitional areas between buildings and walkways.
  • Demonstrated a faster, more cost-effective, and comprehensive assessment compared to traditional manual methods.

Conclusions:

  • The proposed virtual robotic approach offers an efficient and accurate solution for automated pedestrian accessibility assessment.
  • Its segment-independent architecture and continuous data collection support personalized assessments and city-scale deployment.
  • This technology has the potential to significantly improve urban planning and inclusivity for individuals with disabilities.