AutoMerge：一种用于城市尺度环境的地图拼接与平滑框架
AutoMerge: A Framework for Map Assembling and Smoothing in City-scale Environments
AI总结：
提出AutoMerge框架，通过多视角融合、自适应闭环检测和增量合并技术，实现城市尺度无GPS辅助下大规模LiDAR地图片段的鲁棒拼接与全局平滑。
摘要：
我们提出了AutoMerge，一个用于将大量地图片段拼接成完整地图的LiDAR数据处理框架。传统的大规模地图合并方法对错误的数据关联很脆弱，且主要局限于离线工作。AutoMerge利用多视角融合和自适应闭环检测实现准确的数据关联，并使用增量合并从随机顺序给出且无初始估计的单独轨迹片段中组装大地图。此外，在拼接片段后，AutoMerge执行精细匹配和位姿图优化以全局平滑合并后的地图。我们在城市尺度合并（120km）和校园尺度重复合并（4.5km x 8）上展示了AutoMerge。实验表明，AutoMerge (i) 在片段检索中比第二和第三好的方法分别高出14%和24%的召回率，(ii) 在120km大规模地图组装中实现了可比的3D建图精度，(iii) 并且对时间间隔的重访具有鲁棒性。据我们所知，AutoMerge是第一个能够在没有GPS辅助下合并数百公里单独片段的建图方法。
英文摘要：
We present AutoMerge, a LiDAR data processing framework for assembling a large number of map segments into a complete map. Traditional large-scale map merging methods are fragile to incorrect data associations, and are primarily limited to working only offline. AutoMerge utilizes multi-perspective fusion and adaptive loop closure detection for accurate data associations, and it uses incremental merging to assemble large maps from individual trajectory segments given in random order and with no initial estimations. Furthermore, after assembling the segments, AutoMerge performs fine matching and pose-graph optimization to globally smooth the merged map. We demonstrate AutoMerge on both city-scale merging (120km) and campus-scale repeated merging (4.5km x 8). The experiments show that AutoMerge (i) surpasses the second- and third- best methods by 14% and 24% recall in segment retrieval, (ii) achieves comparable 3D mapping accuracy for 120 km large-scale map assembly, (iii) and it is robust to temporally-spaced revisits. To the best of our knowledge, AutoMerge is the first mapping approach that can merge hundreds of kilometers of individual segments without the aid of GPS.
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。
