论文标题
人群源场景更改检测和本地地图更新
Crowd Source Scene Change Detection and Local Map Update
论文作者
论文摘要
随着场景随时间映射的变化而变化，描述符变得过时，从而影响VPS的定位精度。在这项工作中，我们提出了一种检测结构和纹理场景更改的方法，随后是地图更新。在我们的方法中 - 映射包括3D点，具有通过LIDAR或SFM生成的描述符。常见的方法遭受缺点：1）对变更检测的两个点云的直接比较很慢，因为每次我们想比较时都需要构建新的点云； 2）基于图像的比较需要保持地图图像添加大量存储开销。为了解决这个问题，我们提出了一种基于点云的描述符比较的方法：1）基于VPS姿势选择近距离查询和映射图像对，2）对查询映像的注册到映射图像描述符，3）使用分段来过滤动态或短期时间更改，4）比较相应细分之间的描述符。
As scene changes with time map descriptors become outdated, affecting VPS localization accuracy. In this work, we propose an approach to detect structural and texture scene changes to be followed by map update. In our method - map includes 3D points with descriptors generated either via LiDAR or SFM. Common approaches suffer from shortcomings: 1) Direct comparison of the two point-clouds for change detection is slow due to the need to build new point-cloud every time we want to compare; 2) Image based comparison requires to keep the map images adding substantial storage overhead. To circumvent this problems, we propose an approach based on point-clouds descriptors comparison: 1) Based on VPS poses select close query and map images pairs, 2) Registration of query images to map image descriptors, 3) Use segmentation to filter out dynamic or short term temporal changes, 4) Compare the descriptors between corresponding segments.
