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A Robust Laser Stripe Extraction Method for Structured-Light Vision Sensing.

Congyang Zhao¹, Jianing Yang¹, Fuqiang Zhou¹

¹Key Laboratory of Precision Opto-mechatronics Technology, Ministry of Education, Beihang University, Beijing 100191, China.

Sensors (Basel, Switzerland)

|August 23, 2020

Summary

This summary is machine-generated.

This study introduces a new, low-cost visual navigation sensor for robots and autonomous vehicles. The system uses a novel laser-stripe-detection neural network (LSDNN) for robust environmental sensing, even in challenging conditions.

Keywords:

laser stripe extraction semantic segmentation structured-light vision sensor

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Area of Science:

Robotics and Automation
Computer Vision
Sensor Technology

Background:

Environmental sensing is crucial for autonomous systems like cars, drones, and robots.
Traditional vision sensors struggle in low-light conditions, and multi-line LiDAR is expensive.
There is a need for cost-effective and robust environmental sensing solutions.

Purpose of the Study:

To propose a novel, inexpensive visual navigation sensor for environment sensing.
To develop a robust method for laser stripe extraction and 3D reconstruction.
To evaluate the sensor's performance in complex environments.

Main Methods:

Development of a laser-stripe-detection neural network (LSDNN) for robust laser stripe extraction, handling reflective and haze noise.
Utilizing a gray-gravity approach for precise laser stripe center extraction.
Employing a structured-light model for point cloud reconstruction of the laser center.
Designing and optimizing a single-line structured-light sensor and integrating it onto a car platform for evaluation.

Main Results:

The LSDNN effectively extracts laser stripe regions, eliminating noise interference.
The gray-gravity approach accurately determines the laser stripe center.
The structured-light sensor successfully reconstructs point clouds.
Experimental evaluation on a car platform demonstrated the method's effectiveness, accuracy, and robustness in complex environments.

Conclusions:

The proposed structured-light vision sensor offers a cost-effective and robust solution for environmental sensing in autonomous systems.
The LSDNN and gray-gravity approach provide reliable laser stripe detection and center extraction.
The system performs well in challenging conditions, outperforming existing methods in accuracy and robustness.