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  3. Context-aware Semantic Localization With Adaptive Sensor Fusion Under Adverse Conditions.
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  3. Context-aware Semantic Localization With Adaptive Sensor Fusion Under Adverse Conditions.

Related Experiment Video

Design and Analysis for Fall Detection System Simplification
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Context-Aware Semantic Localization with Adaptive Sensor Fusion Under Adverse Conditions.

Jun-Hyeon Choi1, Dong-Su Seo1, Ye-Chan An1

  • 1Department of Electrical and Computer Engineering, College of Information and Communication Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea.

Sensors (Basel, Switzerland)
|February 27, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces semantic localization for autonomous vehicles, improving accuracy and reliability. By integrating semantic reasoning, it significantly reduces localization errors, especially during sensor issues.

Keywords:
context-awareontologysemantic localizationsemantic mapsensor fusion

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Vision

Background:

  • Autonomous driving requires robust vehicle localization under challenging real-world conditions.
  • Conventional methods struggle with sensor degradation and ambiguous data, leading to inaccurate pose estimates.

Purpose of the Study:

  • To develop a semantic localization framework integrating ontology-based reasoning for enhanced accuracy and reliability.
  • To address limitations of geometric-based localization in degraded sensor conditions.

Main Methods:

  • Reformulated localization as a context-aware constraint selection problem.
  • Integrated semantic reasoning to evaluate logical and contextual validity of pose hypotheses.
  • Employed ontology-based semantic consistency checks for objects, places, and vehicle poses.

Main Results:

  • Achieved an average reduction of 35.6% in mean localization error and 47.0% in maximum localization error.
  • Demonstrated improved robustness and accuracy, particularly under sensor degradation and dynamic environments.
  • Enabled structured multi-sensor fusion by selecting semantically relevant information.

Conclusions:

  • Semantic localization significantly enhances autonomous vehicle positioning accuracy and reliability.
  • The framework reduces computational complexity and is adaptable to cooperative perception systems.
  • Ontology-based semantic reasoning is crucial for next-generation autonomous driving localization.