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:我们介绍AutoDAR数据处理框架AutoMeorge, 用于将大量地图段组装成完整的地图。传统的大规模地图合并方法对不正确的数据协会来说是脆弱的,主要限于离线工作。 AutoMeorge为精确的数据协会使用多透视聚合和适应性环闭探测来进行精确的数据协会,它使用递增合并来收集单个轨道段按随机顺序排列的大型地图,没有初步估计。此外,AutoMege在集合各段之后,进行精细的匹配和面容优化,以便全球顺利地进行合并的地图。我们展示城市规模合并(120公里)和校园规模重复合并(4.5公里x8)的自动Meorge(4.5公里x8)。实验显示,AutoMemere (一) 在部分检索中超过第二和第三个最佳方法的14%和24%, (二) 在120公里的大型地图组装中达到可比的3D绘图精度, (三) 并且对时间空间重的重新勘查十分可靠。据我们所知,Aut Merge是第一个可以将单个段数公里的地图合并的方法。