π-Map: A Decision-Based Sensor Fusion with Global Optimization for Indoor Mapping
π-Map: A Decision-Based Sensor Fusion with Global Optimization for Indoor Mapping
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π-Map：基于决策的传感器融合与室内测绘全局优化
DOI：
10.1109/iros45743.2020.9341798
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Chen Liu
中科院分区：
文献类型：
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作者：
Zhiliu Yang;Bo Yu;Wei Hu;Jie Tang;Shaoshan Liu;Chen Liu
在本文中，我们提出了π-map，一种紧密耦合的融合机制，动态消耗LiDAR和声纳数据，为自主机器人导航生成可靠和可扩展的室内地图。π-map的主要新奇在于融合机制的使用，融合机制分为三个阶段：第一阶段LiDAR扫描匹配有效地生成初始关键定位位姿;第二阶段优化用于消除前一阶段积累的错误，并保证可以生成准确的大规模地图;则最终重访扫描融合阶段有效地融合LiDAR地图和声纳地图以生成室内环境的高度精确的表示。我们在大环境和小环境下评估了π-map，并验证了其相对于现有融合方法的优越性。
In this paper, we propose π-map, a tightly coupled fusion mechanism that dynamically consumes LiDAR and sonar data to generate reliable and scalable indoor maps for autonomous robot navigation. The key novelty of π-map over previous attempts is the utilization of a fusion mechanism that works in three stages: the first LiDAR scan matching stage efficiently generates initial key localization poses; the second optimization stage is used to eliminate errors accumulated from the previous stage and guarantees that accurate large-scale maps can be generated; then the final revisit scan fusion stage effectively fuses the LiDAR map and the sonar map to generate a highly accurate representation of the indoor environment. We evaluate π-map on both large and small environments and verify its superiority over existing fusion methods.