The lack of digital floor plans is a huge obstacle to pervasive indoor location based services (LBS). Recent floor plan construction work crowdsources mobile sensing data from smartphone users for scalability. However, they incur long time (e.g., weeks or months) and tremendous efforts in data collection. In this paper, we propose BatMapper, which explores a previously untapped sensing modality–acoustics–for fast, fine grained, and low cost floor plan construction. We design sound signals suitable for heterogeneous microphones on commodity smartphones, and acoustic signal processing techniques to produce accurate distance measurements to nearby objects. We further develop robust probabilistic echo-object association, recursive outlier removal, and probabilistic resampling algorithms to identify the correspondence between distances and objects, thus the geometry of corridors and rooms. We compensate minute hand sway movements to identify small surface recessions, thus detecting doors automatically. Experiments in real buildings show BatMapper achieves $1-2$1-2 $cm$cm distance accuracy in ranges up around 4 $m$m; a $2\sim 3$2∼3 minute walk generates fine grained corridor shapes, detects doors at 92 percent precision and $1\sim 2$1∼2 $m$m location error at 90-percentile; and tens of seconds of measurement gestures produce room geometry with errors $<0.3$<0.3 $m$m at 80-percentile, at $1-2$1-2 orders of magnitude less data amounts and user efforts.

中文翻译：

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使用智能手机上的声学实现可扩展的室内地图构建和细化

缺乏数字平面图是普及室内定位服务 (LBS) 的巨大障碍。最近的平面图施工工作从智能手机用户那里众包移动传感数据以实现可扩展性。然而，它们在数据收集方面需要很长时间（例如，数周或数月）和巨大的努力。在本文中，我们提出蝙蝠映射器，它探索了一种以前未开发的传感模式——声学——用于快速、细粒度和低成本的平面图构建。我们设计了适用于商用智能手机上的异构麦克风的声音信号，以及声学信号处理技术，以产生对附近物体的准确距离测量。我们进一步开发了稳健的概率回声对象关联、递归异常值去除和概率重采样算法来识别距离和对象之间的对应关系，从而识别走廊和房间的几何形状。我们补偿微小的指针摆动以识别小的表面凹陷，从而自动检测门。在真实建筑中的实验表明蝙蝠映射器 达到 $1-2$1——2 $厘米$C米 范围内的距离精度高达 4 百万美元米; 一种$2\sim 3$2～3 分钟步行生成细粒度的走廊形状，以 92% 的精度检测门并 $1\sim 2$1～2 百万美元米90% 的位置错误；和数十秒的测量手势产生错误的房间几何形状$<0.3$<0.3 百万美元米 在 80% 处，在 $1-2$1——2 数据量和用户工作量减少了几个数量级。