面向大规模多-session基于图的SLAM的在线全局闭环检测
Online Global Loop Closure Detection for Large-Scale Multi-Session Graph-Based SLAM
AI总结：
本文提出一种结合全局闭环检测的基于图的SLAM系统，采用内存管理方法仅考虑部分地图以满足在线处理要求，解决了大规模多-session建图中的未知初始定位问题。
摘要：
对于大规模和长期同步定位与建图（SLAM），机器人必须处理由绑架机器人问题或多-session建图引起的未知初始定位问题。本文通过将SLAM系统与全局闭环检测方法相结合来解决这些问题，该方法本质上能够处理这些情况。然而，全局闭环检测方法的在线处理通常受环境规模的影响。所提出的基于图的SLAM系统采用一种内存管理方法，仅考虑地图的一部分以满足在线处理要求。该方法通过使用配备激光测距仪和Kinect的机器人对一栋建筑进行五次室内建图session进行了测试和验证。
英文摘要：
For large-scale and long-term simultaneous localization and mapping (SLAM), a robot has to deal with unknown initial positioning caused by either the kidnapped robot problem or multi-session mapping. This paper addresses these problems by tying the SLAM system with a global loop closure detection approach, which intrinsically handles these situations. However, online processing for global loop closure detection approaches is generally influenced by the size of the environment. The proposed graph-based SLAM system uses a memory management approach that only consider portions of the map to satisfy online processing requirements. The approach is tested and demonstrated using five indoor mapping sessions of a building using a robot equipped with a laser rangefinder and a Kinect.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
