%0 Journal Article
%T 城市场景重访车载点云位置一致性改正<br>Position Consistency Correction of Revisit Mobile Laser Scanning Point Cloud in Urban Scene
%A 邹响红
%A 杨必胜
%A 李健平
%A 董震
%J Geomatics Science and Technology
%P 101-111
%@ 2329-7239
%D 2019
%I Hans Publishing
%R 10.12677/GST.2019.72015
%X 车载激光点云在城市道路资产管理、高精驾驶地图、农村宅基地调查、高速公路改扩建、智能交通等国家重大工程应用中发挥着非常重要的作用；然而，受道路环境复杂、定位信号受遮挡、定姿误差累积等影响，导致往返或不同时相的重访车载点云存在分米甚至米级的位置偏差，严重影响后续数据处理与应用。为解决上述技术瓶颈，本文提出一种城市场景重访车载点云位置一致性改正算法。首先，依据车载轨迹的加速度与角速度将车载点云数据进行层次化分段，同时保证重访段的重叠度；然后，提取分段内的二进制形状上下文(Binary Shape Context, BSC)特征，并依据视觉单词与先验信息加速同名BSC特征匹配；最后，依次进行重访粗分段和细分段点云的两两配准，并剔除不可靠的两两配准结果。实验表明，本文方法能有效改正城市场景重访车载点云中的位置不一致问题，对于不同偏差级别和时相的车载点云，具有很高的鲁棒性和时间效率。<br />
The Mobile Laser Scanning (MLS) point clouds play a very important role in national major engi-neering applications such as urban road asset management, high-definition driving map, rural homestead survey, highway reconstruction and expansion. However, owing to complex road envi-ronment, occluded positioning signal and time-accumulation of attitude error, the MLS point clouds collected by the back and forth scans or among multiple excursions in the same region often suffer misalignment ranging from sub-meter to meters, which impedes the subsequent processing and ap-plications. To deal with the technical bottleneck mentioned above, a method of MLS point cloud po-sition consistency correction in urban scene is proposed. Firstly, the MLS point clouds are divided into multi-scale sub-regions based on the acceleration and angular velocity of each trajectory point, and the overlap degree of revisited sub-regions is ensured at the same time. Secondly, binary shape context (BSC) features in sub-regions are extracted, and visual words and prior information are used to accelerate feature matching. Thirdly, pairwise registration of large and small revisited sub-regions is carried out in turn, and unreliable registration results are removed. The perfor-mance of the proposed method is evaluated on several challenging MLS point clouds with different deviation levels and different temporal, showing good robustness, accuracies and efficiencies.
%K 城市场景，两两配准，位置一致性改正，车载点云<br>Urban Scene
%K Pairwise Registration
%K Position Consistency Correction
%K Mobile Laser Scanning Point Cloud
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=29657