Chai Ziqi, Liu Chao, Xiong Zhenhua
State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Hai'an Institute of Intelligent Equipment, Shanghai Jiao Tong University, Nantong 226600, China.
Sensors (Basel). 2024 Sep 28;24(19):6288. doi: 10.3390/s24196288.
Place recognition is widely used to re-localize robots in pre-built point cloud maps for navigation. However, current place recognition methods can only be used to recognize previously visited places. Moreover, these methods are limited by the requirement of using the same types of sensors in the re-localization process and the process is time consuming. In this paper, a template-matching-based global re-localization framework is proposed to address these challenges. The proposed framework includes an offline building stage and an online matching stage. In the offline stage, virtual LiDAR scans are densely resampled in the map and rotation-invariant descriptors can be extracted as templates. These templates are hierarchically clustered to build a template library. The map used to collect virtual LiDAR scans can be built either by the robot itself previously, or by other heterogeneous sensors. So, an important feature of the proposed framework is that it can be used in environments that have never been visited by the robot before. In the online stage, a cascade coarse-to-fine template matching method is proposed for efficient matching, considering both computational efficiency and accuracy. In the simulation with 100 K templates, the proposed framework achieves a 99% success rate and around 11 Hz matching speed when the re-localization error threshold is 1.0 m. In the validation on The Newer College Dataset with 40 K templates, it achieves a 94.67% success rate and around 7 Hz matching speed when the re-localization error threshold is 1.0 m. All the results show that the proposed framework has high accuracy, excellent efficiency, and the capability to achieve global re-localization in heterogeneous maps.
地点识别被广泛用于在预先构建的点云地图中重新定位机器人以进行导航。然而，当前的地点识别方法仅能用于识别先前访问过的地点。此外，这些方法受到在重新定位过程中使用相同类型传感器的要求限制，并且该过程耗时。本文提出了一种基于模板匹配的全局重新定位框架来应对这些挑战。所提出的框架包括离线构建阶段和在线匹配阶段。在离线阶段，虚拟激光雷达扫描在地图中进行密集重采样，并可提取旋转不变描述符作为模板。这些模板被分层聚类以构建模板库。用于收集虚拟激光雷达扫描的地图既可以由机器人自身先前构建，也可以由其他异构传感器构建。因此，所提出框架的一个重要特征是它可用于机器人之前从未访问过的环境。在在线阶段，提出了一种级联的由粗到精的模板匹配方法以实现高效匹配，同时兼顾计算效率和准确性。在使用10万个模板的模拟中，当重新定位误差阈值为1.0米时，所提出的框架实现了99%的成功率和约11赫兹的匹配速度。在使用4万个模板的新学院数据集上进行验证时，当重新定位误差阈值为1.0米时，它实现了94.67%的成功率和约7赫兹的匹配速度。所有结果表明，所提出的框架具有高精度、卓越的效率以及在异构地图中实现全局重新定位的能力。
