一种鲁棒的月面宽基线图像特征匹配方法

A Robust Feature Matching Method for Wide-Baseline Lunar Images

针对目前图像匹配算法在月面宽基线、弱纹理和光照变化等条件下匹配成功率低的问题,提出基于视图合成与全局注意力的月面图像匹配方法。首先对同站点月面双目图像使用稀疏视差虚真值训练立体匹配网络,完成同站点图像的三维重建。基于场景深度,结合站点之间惯导先验位姿将待匹配图像转为新的合成视图用于匹配,解决不同站点宽基线图像对之间图像重叠度低、视角变化大等问题。进一步使用基于Transformer的图像匹配网络,提高弱纹理场景下的图像匹配性能,并在后处理阶段引入考虑平面退化的外点滤除方法。在真实月面宽基线图像数据集的结果表明,相比现有算法,提出的匹配算法大幅度提高了宽基线场景下的月面图像匹配精度与成功率,为月球车大跨度行驶中的自主视觉定位提供了重要基础。

Objective The vision-based navigation and localization system of China's"Yutu"lunar rover is controlled by a ground teleoperation center.A large-spacing traveling mode with approximately 6-10 m per site is adopted for the rover to maximize the driving distance of the lunar rover and improve the efficiency of remote control exploration.This results in a significant distance between adjacent navigation sites,and considerable rotation,translation,and scale changes in the captured images.Furthermore,the low overlap between images and the vast differences in regional shapes,combined with weak texture and illumination variations on the lunar surface,pose challenges to image feature matching among different sites.Currently,the"Yutu"lunar rover employs inertial measurements and visual matches among different sites for navigation and positioning.The ground teleoperation center adopts inertial measurements as initial poses and optimizes the poses with visual matches by bundle adjustment to obtain the final rover poses.However,due to the wide baseline and significant surface changes of images at different sites,manual assistance is often required to filter or select the correct matches,significantly affecting the efficiency of the ground teleoperation center.Therefore,improving the robustness of image feature matching between different sites to achieve automatic visual positioning is an urgent problem to be addressed.Methods To address the poor performance and low success rate of current image matching algorithms in wide-baseline lunar images with weak textures and illumination variations,we propose a global attention-based lunar image matching algorithm by the view synthesis.First,we utilize sparse feature matching methods to generate sparse pseudo-ground-truth disparities for the rectified stereo lunar images at the same site.Next,we finetune a stereo matching network with these disparities and perform 3D reconstruction for the lunar images at the same site.Then,we leverage inertial measurements among different sites to convert the original image into a new synthetic view for matching based on the scene depth,addressing the low overlap and large viewpoint changes among images of different sites.Additionally,we adopt a Transformer-based image matching network to improve matching performance in weak-texture scenes,and an outlier rejection method that considers plane degeneration in the post-processing stage.Finally,the matches are returned from the synthetic image to the original image,yielding the matches for wide-baseline lunar images at different sites.Results and Discussions We conduct experiments on the real lunar dataset from the"Yutu 2"lunar rover(referred to as the Moon dataset),which includes two parts.The first part is stereo images from five continuous stations(employed for stereo reconstruction),and the second is 12 sets of wide-baseline lunar images from adjacent sites(for wide-baseline image matching testing).In terms of lunar 3D reconstruction,we calculate the reconstruction error within different distance ranges,where the reconstruction network GwcNet(Moon)yields the best reconstruction accuracy and reconstruction details,as shown in Table 1 and Fig.4.Meanwhile,Fig.5 illustrates the synthetic images obtained from the view synthesis scheme based on the inertial measurements between sites and the scene depth,which solves the large rotation,translation,and scale changes between adjacent sites.For wide-baseline image matching,existing algorithms such as LoFTR and ASIFT have matching success rates of 33.33%and 16.67%respectively as shown in Table 2.Our DepthWarp-LoFTR algorithm achieves a matching success rate of 83.33%,significantly improving the matching success rate and accuracy of wide-baseline lunar images(Table 3).Additionally,this algorithm increases the matching success rate from 16.67%to 41.67%compared to the ASIFT algorithm.We present the matching results of different algorithms in Fig.7,where DepthWarp-LoFTR obtains more consistent and denser matching results compared to other methods.Conclusions We propose a robust feature matching method DepthWarp-LoFTR for wide-baseline lunar images.For stereo images captured at the same site,the sparse disparities are generated through a sparse feature matching algorithm.These disparities serve as pseudo-ground truth to train the GwcNet network for 3D reconstruction of lunar images at the same site.To handle the wide baseline and low overlap of images from different sites,we propose a view synthesis algorithm based on scene depth and inertial prior poses.Image matching is performed on the synthesized current-site image and the next-site image to reduce the feature matching difficulty. For the feature matching stage, we adopt a Transformer-based LoFTR network, which significantly improves the success rate and accuracy of automatic matching. Our experimental results on real lunar datasets demonstrate that the proposed algorithm greatly improves the success rate of feature matching in complex lunar wide-baseline scenes. This lays a solid foundation for automatic visual positioning of the "Yutu 2" lunar rover and subsequent routine patrols of lunar rovers in China's fourth lunar exploration phase.