Abstract Today, both point cloud and imagery datasets processed for mapping aims. The precise fusion of both datasets is a major issue that leads to the fine registration problem. This article proposes a fine registration method based on a novel concept of tie plane. The assumption of our solution is that the laser scanner point cloud is much more accurate than the image interior and exterior geometric accuracy. In fact, we register the inaccurate image network to the accurate point cloud data. To do this, tie points are extracted from images. Then, the fine registration is commenced by filtering the unstable tie points as the preprocessing phase. Subsequently, tie planes are reconstructed around the remaining tie points by photogrammetric space intersection. The tie planes are locally fitted to the point cloud data via both normal and directional vectors. Afterward, a novel combined bundle adjustment is developed based on the conventional tie point equations and the new tie plane constraints. Therefore, the interior and exterior orientation parameters are refined. To evaluate our solution, both indoor and outdoor datasets are experimented. The results illustrate a registration error of about <1.6 pixels for both datasets, indicating ∼23% to 40% average accuracy improvement compared to the existing methods.

中文翻译：

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一种新的基于连接平面的影像和点云数据集精细配准方法

摘要 今天，点云和图像数据集都为制图目的进行了处理。两个数据集的精确融合是导致精细配准问题的主要问题。本文提出了一种基于新的连接平面概念的精细配准方法。我们解决方案的假设是激光扫描仪点云比图像内部和外部几何精度要准确得多。实际上，我们将不准确的图像网络注册到准确的点云数据中。为此，从图像中提取连接点。然后，通过过滤不稳定的连接点作为预处理阶段开始精细配准。随后，通过摄影测量空间交集在剩余的连接点周围重建连接平面。连接平面通过法向矢量和方向矢量局部拟合到点云数据。然后，基于传统的连接点方程和新的连接平面约束开发了一种新的组合束平差。因此，对内部和外部方向参数进行了细化。为了评估我们的解决方案，我们对室内和室外数据集进行了实验。结果表明，两个数据集的配准误差约为 <1.6 个像素，与现有方法相比，平均精度提高了 23% 到 40%。