Point clouds are fine for visualization to provide context, but to be really useful they need to be classified. Feature extraction, as the process is often referred to, is currently a mostly manual process that has been used for many years in the remote sensing sector. A trained person visually inspects the point cloud and graphically selects features which are identified by type of feature, sign, light pole, lane marking, and so on.
Automated classification of point clouds
Image processing of 3D point clouds has advanced to the point where it has been possible to semi-automate this tedious feature extraction process. The reliability of the automation process depends on the point density of point cloud, often expressed as points per square meter or per square centimeter. Point density depends on the technology used to capture the point cloud. Terrestrial lidar, for example, typically captures points at the rate of hundreds of thousands to millions of point per second and is capable of millimeter to centimeter precision. Phodar generates lower point densities and is less precise. Aerial lidar captures many points per second but with less precision because it is covering much greater areas. Single photon lidar is a new technology that can collect 6 million points per second which means that planes can fly quite high, cover very broad areas and still collect 12 - 30 points per square meter.
At the Year in Infrastructure 2017 (YII2017) conference in Singapore, Sharad Oberoi of Sanborn gave an insightful overview of the state of the art for automated feature extraction from combined aerial and mobile scans of highways and adjacent areas. Different types of laser scanners are appropriate for capturing different types of features. Terrestrial and mobile laser scanners can capture point clouda that are suitable for extracting curbs, gutter drains, traffic signals, road and local terrain information, parking meters, walls, obstructions in the right of way, manholes, sidewalks, overhead clearances, lights, utility wires and connections, garbage cans, benches, fences, guardrails and barriers, retaining walls, and line of sight vegetation obstructions. Aerial lidar will only be able to get gutter drains, road and local terrain information, walls, obstructions in the right of way, and sidewalks with limited ability to get curbs, lights, utility wires and connections, retaining walls, and vegetation. Lidar does not do well for metal structures such as communications towers and fir these a combination of lidar and phodar is preferred.
Sanborn has developed feature extraction technology that extracts transportation objects from combined aerial and mobile data sets in a semi-automated manner. The mobile and aerial data sets meet the accuracy and information content required for geospatial information for mapping applications as well as the additional information that can be mined for potential asset inventory and infrastructure information content. Mobile technology allows for the low-risk, rapid collection of geospatial information, limiting safety impacts to workers. The aerial LiDAR allows for the rapid collection of elevation information for detailed surface modeling as well as feature extraction. Both technologies collect enormous amounts of point-cloud data that can be merged using the Terrascan capability in Bentley MicroStation. The combined data set is then passed through the automated feature extraction modules to identify transportation objects. Sanborn feature extraction modules achieve a high proportion of correct feature extraction for road and highway scans.
In the future Sharad expects that it will be possible to improve data collection efficiency by using terrestrial robots or autonomous UAVs equipped with SLAM technology to enable them to operate in GPS-denied areas such as under bridges and in tunnels. Sanborn has been working with deep learning to improve the automated extraction process. So far this has yielded mixed results, but Sharad expressed optimism that with AI full automation of the classification process will be achieved.
High precision maps for autonomous vehicles
An application of this technology is high precision maps for the autonomous vehicle market.
Sanborn develops high precision maps for the autonomous vehicle market. Autonomous vehicles will require high precision maps which contain significantly more detailed information and true-ground-absolute accuracy than current road maps. Sanborn has developed proprietary mapping technology that leverages aerial imagery, aerial lidar data, and mobile lidar data to create standardized, high-precision 3D base-maps focusing specifically on the self-driving vehicle market. These maps include detailed inventories of all stationary physical assets related to roadways such as road lanes, road edges, shoulders, dividers, traffic signals, signage, paint markings, poles, and all other critical data needed for the safe navigation of roadways and intersections by autonomous vehicles.
Subscribe
Comments