Lidar-Based Semantic Mapping of Indoor Environments

基于激光雷达的室内环境语义映射

• 批准号: 558367-2020
• 负责人: Kelly, Jonathan
• 金额: $ 2.48万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=723200
• 关键词: Lidar; Based; Semantic; Mapping; Indoor

Light detection and ranging (or lidar) sensors have revolutionized the nascent field of autonomous driving. The ability of lidar to generate accurate point cloud maps of the surrounding environment is essential to maintain situational awareness. Although lidar has primarily been deployed for self-driving vehicles to date, there are many other potential application areas. In particular, lidar can play an essential role in large-scale indoor mapping - the rich lidar data stream can be used to identify and track both known and novel objects indoors. Semantic maps, which contain information about individual object instances, are useful for inventory management in warehouses, for example.The Alliance research program will develop breakthrough capabilities in the area of lidar-based semantic mapping for warehousing and intralogistics applications. Intralogistics (which combines warehouse automation and logistics) is a $15 billion industry today and is expected reach $27 billion in size globally by 2025. In partnership with LeddarTech, Inc., the proposed Alliance research program will leverage machine learning to extract unique material and object signatures from lidar data streams in real time, improving the accuracy of semantic maps in cluttered indoor scenes. Enhanced semantic mapping capabilities will enable LeddarTech to enter new markets and to grow its offerings for existing markets, including the AV sector.

光探测和测距（或激光雷达）传感器已经彻底改变了自动驾驶的新生领域。激光雷达生成周围环境的精确点云地图的能力对于保持态势感知至关重要。尽管迄今为止，激光雷达主要用于自动驾驶汽车，但还有许多其他潜在的应用领域。特别是，激光雷达可以在大规模室内测绘中发挥重要作用-丰富的激光雷达数据流可用于识别和跟踪已知和新的室内物体。例如，语义地图包含有关单个对象实例的信息，可用于仓库中的库存管理。联盟研究计划将在基于激光雷达的语义地图领域为仓储和内部物流应用开发突破性功能。内部物流（结合了仓库自动化和物流）目前是一个价值150亿美元的行业，预计到2025年全球规模将达到270亿美元。与LeddarTech，Inc.合作，拟议中的联盟研究计划将利用机器学习，从激光雷达数据流中真实的提取独特的材料和物体特征，提高杂乱室内场景中语义地图的准确性。增强的语义映射功能将使LeddarTech能够进入新市场，并为包括AV行业在内的现有市场增加其产品。