Reference number : GC-2017-Q4-01054, amendment number : , agreement type : G, recipient legal name : Li, Jonathan (University of Waterloo), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Engage Grants for universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : The highly detailed, up-to-date and accurate road maps are required to enable driverless cars to work_x000D_
autonomous. A SUV-borne mobile laser scanning (or LiDAR) system can capture over 600,000 points per_x000D_
second together with 360 degree panoramic images while the SUV moves along the road. Today a commercial_x000D_
mobile LiDAR system can accumulate 100 gigabytes or more of 3D point cloud data in just one day's driving._x000D_
All these point clouds allow to build up a high-definition road map that can help position a self-driving car on_x000D_
the road to an accuracy of within 10-20 cm. Given a huge amount of mobile LiDAR data, the automated and_x000D_
robust software tool that can handle the mobile LiDAR point clouds to support the high-definition road map_x000D_
making and updating in an effective and efficient fashion is urgently needed. The objective of this project is to_x000D_
develop a cost-effective, point-based road surface marking extraction technology using 3D mobile LiDAR_x000D_
point clouds. The software tool to be developed consists of novel algorithms for point cloud preprocessing,_x000D_
automated extraction and classification of pavement markings. The extracted road marking information can_x000D_
then be used to make and update the road databases. The outcomes of this project will provide a novel,_x000D_
cost-effective and time-saving method for making high-definition road maps to support driverless cars on the_x000D_
Canadian roads. Such a new tool can be supplementary or replaceable for current practices of making road_x000D_
maps using either conventional surveys or image-based approaches., program purpose french : The highly detailed, up-to-date and accurate road maps are required to enable driverless cars to work_x000D_
autonomous. A SUV-borne mobile laser scanning (or LiDAR) system can capture over 600,000 points per_x000D_
second together with 360 degree panoramic images while the SUV moves along the road. Today a commercial_x000D_
mobile LiDAR system can accumulate 100 gigabytes or more of 3D point cloud data in just one day's driving._x000D_
All these point clouds allow to build up a high-definition road map that can help position a self-driving car on_x000D_
the road to an accuracy of within 10-20 cm. Given a huge amount of mobile LiDAR data, the automated and_x000D_
robust software tool that can handle the mobile LiDAR point clouds to support the high-definition road map_x000D_
making and updating in an effective and efficient fashion is urgently needed. The objective of this project is to_x000D_
develop a cost-effective, point-based road surface marking extraction technology using 3D mobile LiDAR_x000D_
point clouds. The software tool to be developed consists of novel algorithms for point cloud preprocessing,_x000D_
automated extraction and classification of pavement markings. The extracted road marking information can_x000D_
then be used to make and update the road databases. The outcomes of this project will provide a novel,_x000D_
cost-effective and time-saving method for making high-definition road maps to support driverless cars on the_x000D_
Canadian roads. Such a new tool can be supplementary or replaceable for current practices of making road_x000D_
maps using either conventional surveys or image-based approaches., agreement title english : A Software Tool for Automated Extraction and Classification of Road Surface Markings from Mobile LiDAR Point Clouds, agreement title french : A Software Tool for Automated Extraction and Classification of Road Surface Markings from Mobile LiDAR Point Clouds, agreement number : EGP, agreement value : 25000.0, agreement start date : 2018-02-07, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada