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Updated: Aug 5, 2025

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

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Mapping with Monocular Camera Sensor under Adversarial Illumination for Intelligent Vehicles.

Wei Tian¹, Yongkun Wen¹, Xinning Chu¹

¹School of Automotive Studies, Tongji University, Shanghai 201804, China.

Sensors (Basel, Switzerland)

|March 30, 2023

Summary

This study introduces an unsupervised learning method to enhance monocular visual mapping in challenging low-light conditions. The approach improves keypoint detection and loop closure, significantly reducing scale drift for more accurate intelligent vehicle navigation.

Keywords:

adversarial illumination intelligent vehicle monocular camera sensor scale drift reduction unsupervised keypoint learning visual mapping

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A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

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Published on: January 18, 2020

Area of Science:

Computer Vision
Robotics
Artificial Intelligence

Background:

High-precision maps are crucial for intelligent-driving vehicles.
Monocular cameras offer a flexible and cost-effective solution for visual mapping.
Adversarial illumination significantly degrades monocular visual mapping performance.

Purpose of the Study:

To develop an unsupervised learning approach for robust keypoint detection and description in monocular images under adverse lighting.
To present a loop-closure detection scheme that mitigates scale drift in monocular visual mapping.
To improve the accuracy and reliability of visual mapping for autonomous driving systems in challenging environments.

Main Methods:

Unsupervised learning emphasizing feature point consistency for improved keypoint extraction in dim light.
A robust loop-closure detection integrating feature-point verification and multi-grained image similarity.
Experimental validation on public benchmarks and real-world driving scenarios (underground and on-road).

Main Results:

The unsupervised keypoint detection approach demonstrates robustness against varied illumination conditions.
The proposed method effectively suppresses scale drift in monocular visual mapping.
Mapping accuracy improved by up to 0.14 m in textureless or low-illumination environments.

Conclusions:

The developed unsupervised learning method enhances monocular visual mapping capabilities in challenging illumination.
The integrated loop-closure scheme significantly reduces scale drift, improving localization accuracy for intelligent vehicles.
This work contributes to more reliable and accurate visual mapping for autonomous navigation in diverse and difficult environments.