The condition assessment of road surfaces is essential to ensure their
serviceability while still providing maximum road traffic safety. This paper
presents a robust stereo vision system embedded in an unmanned aerial vehicle
(UAV). The perspective view of the target image is first transformed into the
reference view, and this not only improves the disparity accuracy, but also
reduces the algorithm's computational complexity. The cost volumes generated
from stereo matching are then filtered using a bilateral filter. The latter has
been proved to be a feasible solution for the functional minimisation problem
in a fully connected Markov random field model. Finally, the disparity maps are
transformed by minimising an energy function with respect to the roll angle and
disparity projection model. This makes the damaged road areas more
distinguishable from the road surface. The proposed system is implemented on an
NVIDIA Jetson TX2 GPU with CUDA for real-time purposes. It is demonstrated
through experiments that the damaged road areas can be easily distinguished
from the transformed disparity maps.

本文介绍了一种嵌入在无人机上的稳健立体视觉系统，可以实时准确地评估道路表面的状况，从而支持道路交通的安全。通过改进视角变换和使用双边滤波器来过滤生成的代价体积，以及最小化能量函数，通过单独检测受损道路区域，降低了算法的计算复杂度。

嵌入式无人机实时密集立体视觉用于道路检测

Real-Time Dense Stereo Embedded in A UAV for Road Inspection

Various 3D reconstruction methods have enabled civil engineers to detect
damage on a road surface. To achieve the millimetre accuracy required for road
condition assessment, a disparity map with subpixel resolution needs to be
used. However, none of the existing stereo matching algorithms are specially
suitable for the reconstruction of the road surface. Hence in this paper, we
propose a novel dense subpixel disparity estimation algorithm with high
computational efficiency and robustness. This is achieved by first transforming
the perspective view of the target frame into the reference view, which not
only increases the accuracy of the block matching for the road surface but also
improves the processing speed. The disparities are then estimated iteratively
using our previously published algorithm where the search range is propagated
from three estimated neighbouring disparities. Since the search range is
obtained from the previous iteration, errors may occur when the propagated
search range is not sufficient. Therefore, a correlation maxima verification is
performed to rectify this issue, and the subpixel resolution is achieved by
conducting a parabola interpolation enhancement. Furthermore, a novel disparity
global refinement approach developed from the Markov Random Fields and Fast
Bilateral Stereo is introduced to further improve the accuracy of the estimated
disparity map, where disparities are updated iteratively by minimising the
energy function that is related to their interpolated correlation polynomials.
The algorithm is implemented in C language with a near real-time performance.
The experimental results illustrate that the absolute error of the
reconstruction varies from 0.1 mm to 3 mm.

本文提出了一种高计算效率、稳健性强的新型密集亚像素视差估计算法，通过将目标帧的透视视图变换为基准视图，在保证匹配精度的同时提高了处理速度。此外，还介绍了一种新的基于马尔可夫随机场和快速双边立体的视差全局细化方法，能够进一步提高估计视差图的精度。实验结果表明，重建的绝对误差在 0.1 至 3 毫米之间。