This is personal result for studying Self-Driving Techs. In this tutorial, I'll upload various codes from basic methods(e.g. lidar point projection) to state-of-the-art techs(e.g. deeplearning-based vehicle detection). Mainly, 'velodyne, camera' data-based approach will be discussed but when the time allows, I'll treat stereo vision, too. Also, Kitti-dataset-related simple codes(e.g. load tracklet or velodyne points) are in
kitti_foundation.pycoded by myself.
Before start,
- KITTI site
- refer to KITTI Dataset Paper for the details of data measurement environment
KITTI 2011_09_26_drive_0005 dataset
Velodyne -> Panoramic Image : Convert Velodyne data(model : HDL-64E) to panoramic image.
Velodyne -> Top-View Image : Convert Velodyne data(model : HDL-64E) to Top-view image.
Velodyne to Image Projection : Project Velodyne points(model : HDL-64E) to camera Image.
Display 3D Tracklet : Display 3D Tracklet on image
always welcome any kind of comments and pull-requests
