NSF Convergence Accelerator Track H: Visit Unknown Places Confidently: Mapping for Accessible BuiLt Environments (MABLE)

对于许多残疾人来说，在不熟悉的室内建筑环境中寻路和导航是一项具有挑战性的任务。盲人或弱视用户由于缺乏无障碍标牌且无法创建空间心理地图而陷入困境。行动不便的人发现很难确定无障碍路线并提前了解他们可能遇到的潜在挑战。患有认知障碍的人很难创建或回忆他们可能绘制的心理地图。此类在陌生空间中的挑战是残疾人 (PWD) 持续焦虑的根源，也是平等参与社会和经济生活的障碍。无障碍空间减少了残疾人使用服务和设施的障碍，增加了独立进行的常规活动的范围，融入了劳动力队伍，并提高了生产力。拟议项目的长期目标是通过创建 MABLE（建筑环境无障碍地图）来改善室内建筑环境内和周围残疾人的无障碍环境。 MABLE 将提供室内环境的数字无障碍地图，并提供一个界面，用于根据用户的承受能力和能力在其中进行评估、规划和导航。它还允许用户根据他们的经验和观察来增强地图。设想的用户包括有视觉或行动障碍的人（盲人、弱视、轮椅使用者、拐杖使用者等）以及其他类别的残疾人，以及需要规划和导航帮助的非残疾人。利用室内地图实现可扩展性和可访问性是一项具有挑战性的任务。专注于可扩展的无障碍地图的公司往往会优先考虑不涵盖导航所需的丰富性的功能或产品。专注于无辅助功能的地图绘制的公司强调可扩展的产品，以减少劳动力需求和支持深度学习的图像处理。其他依赖用户贡献来实现可扩展性的技术无法保证信息的丰富性和完整性，并且没有集成可以使用上下文信息实现实时逐向导航和探索的本地化工具。为了实现可扩展性和可访问性，MABLE 建议利用人工智能、建筑建模、机器人技术、AR/VR 视觉场景推理和低功耗消费电子产品的进步。 MABLE 将使用深度学习增强图像处理算法直接从平面图中提取大多数与无障碍相关的信息，并且可以通过机器人测绘和测量以及利益相关者和用户的贡献来增强任何缺失的信息。此外，通过量化一系列室内定位技术的定位性能和成本，MABLE 产品将创建最适合利益相关者的定制本地化部署，从而鼓励更多的采用。该项目将在建筑环境中的无障碍信息收集、处理和评估领域创造新的知识。除了可持续发展模型之外，该项目还将创建新的框架，用于量化无障碍建筑环境的经济效益，包括未来经济增长潜力、成本节约和投资回报的观点。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

Wayfinding and navigation within unfamiliar indoor built environments are challenging propositions for many persons with disabilities. Blind or low-vision users struggle due to the lack of accessible signage and their inability to create a mental map of the space. Individuals with mobility impairments find it difficult to identify accessible routes and know upfront about potential challenges they may encounter. Individuals with cognitive impairments struggle to create or recollect mental maps they may have made. Such challenges in navigating unfamiliar spaces are a source of constant anxiety for persons with disabilities (PWDs) and a barrier to equal participation in social and economic life. Accessible spaces reduce barriers for PWDs to using services and amenities, increasing the range of regular activities conducted independently, integrating into the workforce, and increasing productivity. The proposed project has the long-term goal of improving accessibility for PWDs within and around indoor built environments through the creation of MABLE (Mapping for Accessibility in BuiLt Environments). MABLE will provide digital accessibility maps of indoor environments with an interface for assessing, planning, and navigating within them based on the affordances and capabilities of the user. It will also permit map augmentation by users based on their experiences and observations. Envisioned users include persons with visual or mobility impairments (blind, low vision, wheelchair users, cane users, etc.) as well as other categories of disabilities, and persons without disabilities who desire planning and navigation assistance. Achieving both scalability and accessibility with indoor maps is a challenging proposition. Companies focused on accessibility mapping that can scale tend to prioritize features or offerings that do not cover the richness needed for navigation. Companies that focus on mapping without accessibility emphasize scalable offerings with reduced labor demands and deep learning enabled image processing. Others that rely on user contributions for scalability cannot guarantee richness and completeness of information, and do not integrate localization tools that can enable real-time turn-by-turn navigation and exploration using contextual information. To achieve both scalability and accessibility, MABLE proposes to leverage advances in AI, building modeling, robotics, AR/VR visual scene reasoning, and low-power consumer electronics. MABLE will extract most accessibility-related information directly from floor plans using deep-learning augmented image-processing algorithms, and any missing information can be augmented through robot mapping and surveying, and stakeholder and user contributions. Further, by quantifying the localization performance and costs of an array of indoor localization technologies, the MABLE product will create custom localization deployments that work best for a stakeholder, encouraging greater adoption. The project will create new knowledge in the areas of collection, processing, and evaluation of accessibility information from built environments. In addition to a sustainability model, the project will also create new frameworks for quantifying economic benefits from accessible built environments encompassing perspectives of future economic growth potential, cost savings, and return on investments.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.