School of Mechanical Engineering and Automation, Harbin Institute of Technology Shenzhen, Shenzhen 518055, China.
Department of Mechanical and Automation Engineering, Chinese University of Hong Kong, Hong Kong, China.
Sensors (Basel). 2019 Sep 30;19(19):4252. doi: 10.3390/s19194252.
Map building and map-based relocalization techniques are important for unmanned vehicles operating in urban environments. The existing approaches require expensive high-density laser range finders and suffer from relocalization problems in long-term applications. This study proposes a novel map format called the ClusterMap, on the basis of which an approach to achieving relocalization is developed. The ClusterMap is generated by segmenting the perceived point clouds into different point clusters and filtering out clusters belonging to dynamic objects. A location descriptor associated with each cluster is designed for differentiation. The relocalization in the global map is achieved by matching cluster descriptors between local and global maps. The solution does not require high-density point clouds and high-precision segmentation algorithms. In addition, it prevents the effects of environmental changes on illumination intensity, object appearance, and observation direction. A consistent ClusterMap without any scale problem is built by utilizing a 3D visual-LIDAR simultaneous localization and mapping solution by fusing LIDAR and visual information. Experiments on the KITTI dataset and our mobile vehicle illustrates the effectiveness of the proposed approach.
地图构建和基于地图的重新定位技术对于在城市环境中运行的无人驾驶车辆至关重要。现有的方法需要昂贵的高密度激光测距仪，并在长期应用中存在重新定位问题。本研究提出了一种新的地图格式，称为 ClusterMap，并在此基础上开发了一种实现重新定位的方法。ClusterMap 通过将感知到的点云分割成不同的点簇，并过滤掉属于动态物体的簇来生成。为了区分，为每个簇设计了一个位置描述符。通过在本地地图和全局地图之间匹配簇描述符来实现全局地图中的重新定位。该解决方案不需要高密度点云和高精度分割算法。此外，它还可以防止环境变化对光照强度、物体外观和观察方向的影响。通过融合激光雷达和视觉信息的 3D 视觉激光雷达同时定位和地图构建解决方案，构建了一个没有任何比例问题的一致的 ClusterMap。在 KITTI 数据集和我们的移动车辆上的实验验证了所提出方法的有效性。
