用空间正则表达式快速查询机器人感知数据中的特定模式。
Querying Perception Streams with Spatial Regular Expressions
- 提出SpREs语言，用正则方式匹配时空感知数据中的模式。
- 在296毫秒内查出超过2万个匹配，适合实时监控场景。
- 支持离线分析和在线集成，适用于自动驾驶等动态环境。
机器人、制造和数据分析等领域生成大量时空数据以捕捉环境信息，但针对特定场景筛选数据费时易错，且缺乏通用性和可复现性。本文提出SpREs，一种用于多模态动态环境中时空感知流的新型查询语言。为展示其能力，开发了STREM工具，支持离线与在线模式匹配。通过在公开的Woven Planet Perception自动驾驶数据集上进行离线案例研究，以及在ROS中集成CARLA模拟器的在线案例研究，验证了其有效性。性能基准测试表明，该框架可在296毫秒内找到超过20,000个匹配，适用于实时监控应用。
原文摘要 · Abstract (English)
Perception in fields like robotics, manufacturing, and data analysis generates large volumes of temporal and spatial data to effectively capture their environments. However, sorting through this data for specific scenarios is a meticulous and error-prone process, often dependent on the application, and lacks generality and reproducibility. In this work, we introduce SpREs as a novel querying language for pattern matching over perception streams containing spatial and temporal data derived from multi-modal dynamic environments. To highlight the capabilities of SpREs, we developed the STREM tool as both an offline and online pattern matching framework for perception data. We demonstrate the offline capabilities of STREM through a case study on a publicly available AV dataset (Woven Planet Perception) and its online capabilities through a case study integrating STREM in ROS with the CARLA simulator. We also conduct performance benchmark experiments on various SpRE queries. Using our matching framework, we are able to find over 20,000 matches within 296 ms making STREM applicable in runtime monitoring applications.
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