SBIR Phase I: Leveraging Building Information Modeling (BIM) for More Accurate Indoor Positioning

这项小企业创新研究（SBIR）第一阶段项目的更广泛影响/商业潜力是，能够在病毒爆发期间将病例调查和接触者追踪作为核心疾病控制措施。这项技术使设施能够跟踪并遵守室内空间的容量限制。产生的数据将使设施能够在COVID-19大流行期间病毒传播风险高的情况下确保更高程度的安全。作为新建或翻新的“智能建筑”中可能出现的众多进步之一，这项技术具有市场价值，因为它将为移动计算、物联网和网络物理系统提供创新。SBIR （Small Business Innovation Research）一期项目提出了一种更精确的室内定位方法，利用数值传播模型、语义模型和建筑信息模型（BIM）来解决多目标优化问题。目前可用的跟踪模型不能准确地确定给定空间内居住者的数量和位置。该提案的研究目标涉及使用存储在3D模型中的环境信息，包括墙壁和地板位置，材料组成，尺寸分析和家具布局，以准确地检测居住者。语义信息将根据建筑配置和用户配置文件收集。三个参数：物理障碍，以前的位置和工作功能将被用来计算目标在给定位置的概率。预期的技术成果将是创建一个3D概率地图，可以搜索环境进行实时室内定位。这将使建筑物管理人员能够跟踪在任何给定位置有多少居住者，以更准确地遵守容量限制，特别是在病毒严重爆发的时候。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is the ability for case investigation and contact tracing as core disease control measures during times of viral outbreak. This technology enables facilities to track and adhere to capacity limits for indoor spaces. The data generated will allow facilities to ensure a higher degree of safety during instances of high viral transmission risk as seen during the COVID-19 pandemic. This technology is marketable as one of the many advances possible in newly built or renovated “smart buildings” as it will offer innovations in mobile computing, the Internet of Things, and cyber-physical systems. This Small Business Innovation Research (SBIR) Phase I project proposes a more accurate indoor positioning method, utilizing the numerical propagation model, semantic model, and Building Information Modeling (BIM) to solve multi-objective optimization problems. Currently available tracking models cannot accurately ascertain the number and location of occupants in a given space. The research objectives of this proposal involve using environmental information stored within 3D models including wall and floor location, materials composition, dimensional analysis, and furniture layouts to detect occupants accurately. The semantic information will be gathered based on the building configuration and user profiles. Three parameters: physical obstacles, previous positions, and job functions will be used to calculate the probability of the target being in a given location. The anticipated technical result will be the creation of a 3D probability map that can search the environment for real-time indoor positioning. This will enable building managers to track how many occupants are in any given location to more accurately comply with capacity restrictions, especially in times of severe viral outbreaks.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.