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Development of a GPU-Accelerated NDT Localization Algorithm for GNSS-Denied Urban Areas.
Keon Woo Jang1, Woo Jae Jeong1, Yeonsik Kang1
1Department of Automotive Engineering, Kookmin University, 77 Jeongneung-ro, Seongbuk-gu, Seoul 02707, Korea.
Sensors (Basel, Switzerland)
|March 10, 2022
Summary
This study accelerates light detection and ranging (LiDAR) localization for autonomous driving in GPS-denied areas. The new algorithm significantly boosts computational speed on embedded systems without sacrificing localization accuracy.
Area of Science:
- Robotics
- Computer Vision
- Geospatial Technology
Background:
- Autonomous driving faces localization challenges in urban, global navigation satellite system-denied environments.
- High-resolution light detection and ranging (LiDAR) sensors offer precise distance measurements for improved localization.
- Existing map-matching algorithms for LiDAR localization can be computationally intensive.
Purpose of the Study:
- To develop an algorithm that accelerates LiDAR localization computational speed.
- To maintain the accuracy of lightweight map-matching algorithms during acceleration.
- To enable efficient autonomous vehicle navigation in challenging urban settings.
Main Methods:
- Transformed point cloud maps into normal distribution (ND) maps using vector-based normal distribution transform.
- Implemented graphics processing unit (GPU) parallel processing for the ND map-matching process.
- Validated the algorithm using open datasets, simulations, and real-time embedded system comparisons.
Main Results:
- Achieved a nearly 100-fold increase in computational speed on an embedded computer.
- Maintained high localization precision comparable to original algorithms.
- Demonstrated practical real-time performance through serial and parallel processing comparisons.
Conclusions:
- The proposed algorithm effectively accelerates LiDAR localization for autonomous driving.
- GPU parallel processing of ND maps is a viable method for enhancing real-time performance.
- This advancement is crucial for reliable autonomous navigation in GPS-denied urban areas.
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