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.

翻译：我们提出AutoMerge，一种用于将大量地图片段组装成完整地图的激光雷达数据处理框架。传统的大规模地图合并方法对错误的数据关联较为脆弱，且主要限于离线操作。AutoMerge利用多视角融合和自适应回环检测实现精确的数据关联，并通过增量式合并，从随机顺序且无初始估计的单个轨迹片段中组装大规模地图。此外，在片段组装完成后，AutoMerge执行精细匹配和位姿图优化，以全局平滑合并后的地图。我们在城市规模合并（120公里）和校园规模重复合并（4.5公里×8）上展示了AutoMerge的性能。实验表明，AutoMerge在片段检索中召回率分别超过第二和第三最佳方法14%和24%，在120公里大规模地图组装中实现了可比的3D建图精度，并且对时间间隔较长的重新访问具有鲁棒性。据我们所知，AutoMerge是首个无需GPS辅助即可合并数百公里单个片段的地图构建方法。