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基于深度学习的LiDAR和相机外部校准的审查
Zhiguo Tan1,2, Xing Zhang1,2,3, Shuhua Teng1,2
1School of Electronic Information, Hunan First Normal University, Changsha 410205, China.
Sensors (Basel, Switzerland)
|June 27, 2024
概括
基于学习的LiDAR和摄像头校准方法提供了快速,准确和强大的无目标校准,这对于自主系统至关重要. 本综述对这些方法进行了分类,并讨论了它们的演变,机制和未来方向.
科学领域:
- 机器人技术和自主系统
- 计算机视觉 计算机视觉
- 传感器融合式传感器
背景情况:
- 外在参数校准对于自主系统中的LiDAR和相机数据融合至关重要.
- 应用包括自动驾驶,移动机器人和智能监控.
- 基于学习的方法正在成为一个关键的无目标校准技术.
研究的目的:
- 系统地审查基于学习的LiDAR和相机校准方法的最新进展.
- 根据参数估计原则对这些方法进行分类.
- 讨论它们的演变,机制,优势,局限性和未来趋势.
主要方法:
- 基于学习的校准算法的分类,分为准确估计和相对预测.
- 编制算法演变路线和框架.
- 在算法步骤中使用的方法的总结.
主要成果:
- 基于学习的方法在速度,准确性和稳定性方面具有优势.
- 已经确定了基于学习的校准算法的两个主要类别.
- 详细讨论每个类别的机制,优点,缺点和用例.
结论:
- 基于学习的校准对于LiDAR相机融合变得不可或缺.
- 确定了现有的研究差距和未来发展趋势.
- 该综述为研究人员和从业人员提供了全面的概述.
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