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Semantic Point Cloud Mapping of LiDAR Based on Probabilistic Uncertainty Modeling for Autonomous Driving
Sungjin Cho1, Chansoo Kim1, Jaehyun Park1
1Department of Automotive Engineering, Hanyang University, Seoul 04763, Korea.
Sensors (Basel, Switzerland)
|October 22, 2020
Summary
This study introduces a novel LiDAR SLAM system that incorporates semantic information to improve autonomous driving. By modeling segmentation uncertainty, it enhances mapping accuracy and resolves semantic ambiguities.
Area of Science:
- Robotics
- Computer Vision
- Artificial Intelligence
Background:
- LiDAR-based Simultaneous Localization And Mapping (SLAM) is crucial for autonomous vehicles.
- Semantic information enhances SLAM capabilities beyond geometric data.
- Uncertainty in semantic segmentation limits direct application in LiDAR SLAM.
Purpose of the Study:
- To propose a semantic segmentation-based LiDAR SLAM system that addresses segmentation uncertainty.
- To improve the accuracy and reliability of map generation for autonomous driving.
Main Methods:
- Developed probability models from data-driven approaches to quantify semantic segmentation uncertainty.
- Introduced a semantic registration method using these probability models to calculate point cloud transformations.
- Utilized probability models to resolve semantic class ambiguities in multi-scan point clouds.
Main Results:
- The proposed framework effectively reduces mapping pose errors.
- Ambiguity in semantic information within the generated semantic map is eliminated.
- Experimental validation on the KITTI dataset and outdoor environments demonstrated system efficacy.
Conclusions:
- The developed system successfully integrates semantic information into LiDAR SLAM by managing segmentation uncertainty.
- This approach significantly enhances the performance and reliability of autonomous driving systems.
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