从一系列立体图像中估计雾参数
IEEE transactions on pattern analysis and machine intelligence
|October 27, 2025
概括
这项研究引入了一种新的方法,用于从立体图像中估计雾参数,改进视觉同步定位和映射 (SLAM) 系统. 该方法同时估计雾参数,在现实世界,不均的雾条件下提高准确性.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 环境传感器环境传感器
背景情况:
- 从立体图像中估计雾参数的现有方法通常由于顺序参数估计而遭受错误传播.
- 现实世界的雾通常是全球不均的,对假设局部均性的算法构成挑战.
研究的目的:
- 开发一种新的方法,从立体雾图像同时估计雾模型参数.
- 创建一个全面的数据集,用于在雾条件下评估视觉感知算法.
- 为了提高视觉同步定位和绘图 (SLAM) 和在雾中测距系统的稳定性和准确性.
主要方法:
- 制定了一个新的优化问题,以同时估计所有雾参数,克服顺序方法的局限性.
- 该方法假设雾的局部均性,使全球不均的真实世界雾能够有效处理.
- 开发了真实雾中立体驾驶 (SDIRF) 数据集,其中包括真实雾道路场景的校准立体和相应的晴天数据.
主要成果:
- 拟议的算法在合成和真实雾状数据上表现出优越的性能,与之前的方法相比.
- 同时估计雾参数会导致更准确的结果和更好地适应现实世界雾条件.
- SDIRF数据集为准确的大气散射模型应用提供了必要的校准光度参数.
结论:
- 开发的方法为雾环境中的视觉感知提供了显著的进步.
- 该算法可以无地作为附加模块集成到现有的视觉SLAM和测距系统中.
- 该代码和SDIRF数据集的公开发布旨在促进对雾影响视觉知觉的进一步研究.
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