论文标题
通过单色和彩色摄像头融合的室外环境的轻稳定性单眼估计
Light Robust Monocular Depth Estimation For Outdoor Environment Via Monochrome And Color Camera Fusion
论文作者
论文摘要
深度估计在SLAM，进程和自主驾驶中起着重要作用。尤其是，由于其成本低，记忆和计算，单眼深度估计是有利可图的技术。但是，由于相机通常由于光线条件而无法获得干净的图像，因此这并不足够预测深度图。为了解决这个问题，已经提出了各种传感器融合方法。即使这是一种强大的方法，传感器融合也需要昂贵的传感器，额外的内存和高计算性能。 在本文中，我们提出了颜色图像和单色图像像素级融合以及立体声匹配，并与部分增强的相关系数最大化。我们的方法不仅胜过所有指标的最新方法，而且在成本，内存和计算方面也有效。我们还通过消融研究来验证设计的有效性。
Depth estimation plays a important role in SLAM, odometry, and autonomous driving. Especially, monocular depth estimation is profitable technology because of its low cost, memory, and computation. However, it is not a sufficiently predicting depth map due to a camera often failing to get a clean image because of light conditions. To solve this problem, various sensor fusion method has been proposed. Even though it is a powerful method, sensor fusion requires expensive sensors, additional memory, and high computational performance. In this paper, we present color image and monochrome image pixel-level fusion and stereo matching with partially enhanced correlation coefficient maximization. Our methods not only outperform the state-of-the-art works across all metrics but also efficient in terms of cost, memory, and computation. We also validate the effectiveness of our design with an ablation study.
