论文标题
汽车：在城市规模环境中进行地图组装和平滑的框架
AutoMerge: A Framework for Map Assembling and Smoothing in City-scale Environments
论文作者
论文摘要
我们提出Automerge，这是一种LIDAR数据处理框架，用于将大量地图段组装到完整的地图中。传统的大型地图合并方法对于错误的数据关联是脆弱的，并且主要仅限于离线工作。 Automerge利用多观点融合和自适应环路闭合检测来进行准确的数据关联，并且它使用增量合并来从随机顺序给出的单个轨迹段组装大图，而无初始估计。此外，在组装段后，自动制度可以执行良好的匹配和姿势图片优化，以在全球范围内平滑合并的地图。我们展示了城市规模合并（120公里）和校园规模重复合并（4.5公里x 8）的汽车。该实验表明，自动化（i）在段检索中超过了14％和第三最佳方法，（ii）在120 km大型地图组件（III）中获得了可比较的3D映射精度，并且它是稳健的临时revisits。据我们所知，Automerge是第一种映射方法，它可以在无GPS的帮助下合并数百公里的单个细分市场。
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.
