3D city models: Mapping approach using LIDAR technology

Automobile indexation from 3D point clouds of urban scenarios

“…Points from the ground plane are detected and extracted from the point cloud using the method proposed in [18], then we detect and extract the facades. The extraction of the points corresponding to the ground plane uses a threshold of α.…”

“…Knowing that the facades are perpendicular planes to the ground, we did not use the third coordinate of the segmentation points. Then, using the modified Hough transform [18] we searched for the set of points that model a plane. The parametric space is given by ρ, θ, which are the normal vector parameters that pass through the origin on the modified Hough transform.…”

Simulation of simultaneous localization and mapping using 3D point cloud data

“…LiDAR has become one of the key technologies for remote sensing that is able to sample the entire environment and capture extremely accurate objects very quickly [4][5]. LiDAR is capable to collect more than one million points per second (pps) of high quality three-dimensional (3D) urban data [6]. The sensor is especially useful for accurate 3D of other manmade structures like road details, urban furniture or vegetation [7].…”

“…The most well-known manufacturer of LiDAR is Velodyne [8][9]. The Velodyne High Definition LiDAR (HDL) unit provides a 360-degree azimuth field of view and a 26.5 degree elevation field of view, up to 15 Hz frame refresh rate, and rich point cloud populated at a rate of one million points per second [6]. LiDAR is also capable of recording every single data that bounces back to it every second.…”

“…Due to its robustness, this technology has encouraged many researchers to develop algorithms for scan matching, object detection and mapping [1,[5][6][7]. Since LiDAR applied 3D point cloud data, a good algorithm is needed to fully utilize all the data that has been produced.…”