核心概念
Utilizing CycleGAN for image translation improves radar-LiDAR extrinsic calibration.
要約
The article discusses the importance of sensor data integration in robotics, focusing on extrinsic calibration parameters between radar and LiDAR sensors. It introduces a novel framework using CycleGAN for image-to-image translation to estimate 3-DOF extrinsic parameters. The method addresses challenges like motion distortion and noise in radar data. Experimental results show improved accuracy in extrinsic calibration compared to traditional methods.
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Introduction
- Sensor integration crucial in robotics.
- Importance of extrinsic calibration for sensor fusion.
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Methodology
- Preprocessing radar and LiDAR data.
- Image translation using CycleGAN.
- Image registration with MI and phase correlation.
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Experimental Results
- Evaluation of translated radar images.
- Image registration accuracy.
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Conclusion
- Proposed pipeline enhances radar-LiDAR extrinsic calibration.
- Future applications in place recognition and SLAM.
統計
The use of data fusion between complementary sensors can provide significant benefits.
CycleGAN is utilized for image-to-image translation for extrinsic calibration.
The proposed method demonstrates notable improvement in extrinsic calibration.
引用
"The use of image registration techniques, as well as deskewing based on sensor odometry and B-spline interpolation, is employed to address the rolling shutter effect."
"Our method demonstrates a notable improvement in extrinsic calibration compared to filter-based methods using the MulRan dataset."