Forest is one of the most challenging environments to be recorded in a three-dimensional(3D)digitized geometrical representation,because of the size and the complexity of the environment and the data-acquisition const...Forest is one of the most challenging environments to be recorded in a three-dimensional(3D)digitized geometrical representation,because of the size and the complexity of the environment and the data-acquisition constraints brought by on-site conditions.Previous studies have indicated that the data-acquisition pattern can have more influence on the registration results than other factors.In practice,the ideal short-baseline observations,i.e.,the dense collection mode,is rarely feasible,considering the low accessibility in forest environments and the commonly limited labor and time resources.The wide-baseline observations that cover a forest site using a few folds less observations than short-baseline observations,are therefore more preferable and commonly applied.Nevertheless,the wide-baseline approach is more challenging for data registration since it typically lacks the required sufficient overlaps between datasets.Until now,a robust automated registration solution that is independent of special hardware requirements has still been missing.That is,the registration accuracy is still far from the required level,and the information extractable from the merged point cloud using automated registration could not match that from the merged point cloud using manual registration.This paper proposes a discrete overlap search(DOS)method to find correspondences in the point clouds to solve the low-overlap problem in the wide-baseline point clouds.The proposed automatic method uses potential correspondences from both original data and selected feature points to reconstruct rough observation geometries without external knowledge and to retrieve precise registration parameters at data-level.An extensive experiment was carried out with 24 forest datasets of different conditions categorized in three difficulty levels.The performance of the proposed method was evaluated using various accuracy criteria,as well as based on data acquired from different hardware,platforms,viewing perspectives,and at different points of time.The prop展开更多
本文针对区域覆盖任务需求对多无人机搜索问题展开研究。首先,提出一种任意搜索区域的等面积单侧区域分割方法(Unilateral Region Segmentation)。然后,每个搜索区域分派一架或一个编队的无人机进行扫描线搜索,再基于人工势场法来规避...本文针对区域覆盖任务需求对多无人机搜索问题展开研究。首先,提出一种任意搜索区域的等面积单侧区域分割方法(Unilateral Region Segmentation)。然后,每个搜索区域分派一架或一个编队的无人机进行扫描线搜索,再基于人工势场法来规避障碍物或者威胁从而获得搜索路径。最后,进行仿真分析,验证了该算法在不同情况下的有效性、鲁棒性以及适应性。该算法在面向任意搜索区域、考虑无人机机动性以及存在威胁等问题时具有明显优势。展开更多
基金financial support from the National Natural Science Foundation of China(Grant Nos.32171789,32211530031)Wuhan University(No.WHUZZJJ202220)Academy of Finland(Nos.334060,334829,331708,344755,337656,334830,293389/314312,334830,319011)。
文摘Forest is one of the most challenging environments to be recorded in a three-dimensional(3D)digitized geometrical representation,because of the size and the complexity of the environment and the data-acquisition constraints brought by on-site conditions.Previous studies have indicated that the data-acquisition pattern can have more influence on the registration results than other factors.In practice,the ideal short-baseline observations,i.e.,the dense collection mode,is rarely feasible,considering the low accessibility in forest environments and the commonly limited labor and time resources.The wide-baseline observations that cover a forest site using a few folds less observations than short-baseline observations,are therefore more preferable and commonly applied.Nevertheless,the wide-baseline approach is more challenging for data registration since it typically lacks the required sufficient overlaps between datasets.Until now,a robust automated registration solution that is independent of special hardware requirements has still been missing.That is,the registration accuracy is still far from the required level,and the information extractable from the merged point cloud using automated registration could not match that from the merged point cloud using manual registration.This paper proposes a discrete overlap search(DOS)method to find correspondences in the point clouds to solve the low-overlap problem in the wide-baseline point clouds.The proposed automatic method uses potential correspondences from both original data and selected feature points to reconstruct rough observation geometries without external knowledge and to retrieve precise registration parameters at data-level.An extensive experiment was carried out with 24 forest datasets of different conditions categorized in three difficulty levels.The performance of the proposed method was evaluated using various accuracy criteria,as well as based on data acquired from different hardware,platforms,viewing perspectives,and at different points of time.The prop
文摘本文针对区域覆盖任务需求对多无人机搜索问题展开研究。首先,提出一种任意搜索区域的等面积单侧区域分割方法(Unilateral Region Segmentation)。然后,每个搜索区域分派一架或一个编队的无人机进行扫描线搜索,再基于人工势场法来规避障碍物或者威胁从而获得搜索路径。最后,进行仿真分析,验证了该算法在不同情况下的有效性、鲁棒性以及适应性。该算法在面向任意搜索区域、考虑无人机机动性以及存在威胁等问题时具有明显优势。
