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Updated: Jun 14, 2025
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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
Published on: May 7, 2019
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Object Detection and Information Perception by Fusing YOLO-SCG and Point Cloud Clustering
Chunyang Liu1,2, Zhixin Zhao1, Yifei Zhou1
1School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471003, China.
Sensors (Basel, Switzerland)
|August 29, 2024
Summary
This study introduces a novel robot perception algorithm fusing vision and LiDAR. The YOLO-SCG model enhances object detection and point cloud processing for safer navigation.
Area of Science:
- Robotics
- Computer Vision
- Sensor Fusion
Background:
- Robots require environmental sensing for safe navigation and obstacle avoidance.
- Single-sensor approaches are limited in information acquisition and real-time performance.
Purpose of the Study:
- To develop an advanced information perception algorithm for robots.
- To enhance real-time environmental understanding and path planning capabilities.
Main Methods:
- Proposed a vision-centric algorithm fusing camera data with LiDAR point clouds.
- Developed the YOLO-SCG model for accelerated and precise object detection.
- Integrated vision detection results into LiDAR point cloud processing for improved clustering.
Main Results:
- The YOLO-SCG model demonstrated a 4.06% increase in accuracy and a 7.81% improvement in detection speed over YOLOv9.
- Enhanced point cloud processing speed and detection effectiveness.
- Achieved superior performance in distinguishing objects during point cloud clustering.
Conclusions:
- The proposed vision-LiDAR fusion algorithm significantly improves robot perception.
- YOLO-SCG offers a faster and more accurate solution for object detection in robotics.
- The integrated approach enhances the robot's ability to understand and navigate complex environments.
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