Street View Collection Coverage Optimization

街景集合覆盖范围优化

• 批准号: 521636-2017
• 负责人: Georgiou, Konstantinos(Costis)
• 金额: $ 1.82万
• 依托单位: Ryerson University
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=640142
• 关键词: Street; View; Collection; Coverage; Optimization

Ecopia is an international supplier of high definition (HD) mapping products, which result, among others, fromtraversing actual road networks by self-operated vehicles. Currently, Ecopia's street view vehicle operator usesa self regulated method to collect imagery data in the city of Toronto. This approach utilizes no optimizationmethods with respect to traffic or routing conditions. Instead, the vehicle operator chooses a "target zone" thatthey intend to cover in one work-shift and attempt to finish that using only intuition on how to drive it. Ecopiais looking to expand the imagery collection program across Canada and will require efficient methodologies(algorithms) to traverse road networks so as to optimize the time required for data collection in new cities.

Ecopia是一家高清地图产品的国际供应商，其产品包括通过自主运营的车辆穿越实际道路网络。目前，Ecopia的街景车辆运营商使用自我调节的方法来收集多伦多市的图像数据。这种方法没有利用优化方法的交通或路由条件。相反，车辆操作员选择一个“目标区域”，他们打算在一个轮班覆盖，并试图完成，只使用直觉就如何驾驶it.Ecopiis希望扩大整个加拿大的图像收集程序，将需要有效的方法（算法）遍历道路网络，以优化所需的时间，在新的城市数据收集。