针对传统的单序列扩展式场景重构方法易丢失场景信息, 数据利用率低下等问题, 提出一种面向场景重构的多序列间配准的方法。该方法采用相似变换作为配准模型, 完成对不同参考坐标系下尺度、旋转、平移相分离的初始配准; 然后通过拟合观测平面来抑制噪声点并筛选公共可见点; 最终对不同尺度、方位的序列进行配准, 配准结果可直接用于后续的重构中。实验表明: 通过相应噪声抑制, 使得各序列的系统初始重投影误差降低了17.69%到46.86%, 终止重投影误差降低了27.5%到71.96%。配准后从相同的47幅图像中可重构10 596个场景点, 相比传统单序列方法的3 893个场景点, 该方法更充分有效地利用了观测图像, 使最终拟合的场景曲面包含了更多的场景细节。

As traditional single sequence scene reconstruction method is easy to loss scene information and has a lower data utilization, this paper proposes an approach to the registration between multiple sequences for scene reconstruction. The method uses a similarity transformational as the registration model and proposes the separating initial registration method to deal with the scale, rotation and translation respectively. Then, it fits the observed plane to suppress the noise data and select the co-visible points. Finally, the registration between multiple sequences with different scales, positions is performed and the registration results are applied in successive subsequent reconstruction. The experiment shows that the initial error has been reduced from 17.69% to 46.86%, and the terminated error reduced from 27.5% to 71.96% after suppression of noise data along the oriented orientation. Moreover, the 10 596 vertices are reconstructed from 47 frame images, more better than 3 893 vertices reconstructed by traditional single sequential method. The proposed method full makes use of the observation images and allows the ultimate fitting surface of the scene to have more scene details.