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Related Experiment Video

Updated: Nov 3, 2025

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery
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Semantic Evidential Grid Mapping Using Monocular and Stereo Cameras.

Sven Richter1, Yiqun Wang1, Johannes Beck2

  • 1Institute of Measurement and Control Systems, Karlsruhe Institute of Technology (KIT), Engler-Bunte-Ring 21, 76131 Karlsruhe, Germany.

Sensors (Basel, Switzerland)
|June 2, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a semantic evidential grid mapping pipeline for autonomous vehicles, enhancing scene understanding with vision sensors. It accurately models uncertainties and fuses data for improved traffic scene estimation.

Keywords:
autonomous drivingenvironment perceptiongrid mappingmonocular visionstereo vision

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Area of Science:

  • Computer Vision
  • Robotics
  • Artificial Intelligence

Background:

  • Accurate local traffic scene estimation is crucial for automated vehicle software development.
  • Existing grid mapping methods often rely solely on range sensors (Lidar, Radar) and lack semantic information.
  • Integrating vision-based sensors offers redundancy and richer scene data.

Purpose of the Study:

  • To develop a semantic evidential grid mapping pipeline incorporating vision sensor data.
  • To enable fusion of vision data with existing range sensor data for comprehensive scene representation.
  • To explicitly model uncertainties within the evidential model for robust estimation.

Main Methods:

  • A novel semantic evidential grid mapping pipeline processing monocular and stereo vision data.
  • Incorporation of disparity- or depth-based ground surface estimation for accurate mapping.
  • Explicit modeling of uncertainties in the evidential model.

Main Results:

  • Accurate and dense semantic grid maps generated from vision data.
  • Successful fusion of vision-based semantic information with potential range sensor data.
  • Demonstrated superiority over existing semantic grid mapping approaches in quantitative evaluations.

Conclusions:

  • The proposed pipeline effectively enhances traffic scene understanding for automated vehicles using vision sensors.
  • Explicit uncertainty modeling improves the robustness of semantic grid maps.
  • The approach provides a foundation for sensor-diverse and semantically rich environmental representations.