| Abstract |
We propose a method to reconstruct a High-Definition Color-Pointed map from the large-scale urban road scene. In contrast to prior methods, we collect the urban road data under the severe traffic conditions and several kilometers long data sets. In our High-Definition Color-Pointed map, we can clearly see the lane surface and surrounding environment. Moreover, our maps are collected in a high speed of 40km/h, which greatly improved the practicality of the whole system. The High-Definition Color-Pointed map can be used in perception, localization and automatic navigation in driverless cars, drones and smart city management. Images, lidar and gnss best poses are collected as the raw data. We use interpolation method to process the raw data, and use our algorithm to translate the Lidar poses into the images collected time. To detect the dynamic obstacle, we use the fast neural framework YOLOv2. Our method, Dislocation Projection, can solve the spare points problems. Our algorithm was evaluated on wide roads and narrow streets. The experimental results exhibited the effectiveness of the proposed approach. © 2019 IEEE. |
