Transforming a 3-D LiDAR Point Cloud Into a 2-D Dense Depth Map Through a Parameter Self-Adaptive Framework

The 3-D LiDAR scanner and the 2-D charge-coupled device (CCD) camera are two typical types of sensors for surrounding-environment perceiving in robotics or autonomous driving. Commonly, they are jointly used to improve perception accuracy by simultaneously recording the distances of surrounding obje...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2017-01, Vol.18 (1), p.165-176
Main Authors: Chen, Long, He, Yuhang, Chen, Jianda, Li, Qingquan, Zou, Qin
Format: Article
Language:English
Subjects:
3D-2D conversion
Anisotropic magnetoresistance
Cameras
CCD cameras
Charge coupled devices
Color imagery
dense depth map
global enhancement
Image color analysis
Intelligent vehicle
Laser radar
Lidar
Measurement
Scanners
Space perception
Tensile stress
Three dimensional models
Three-dimensional displays
Two dimensional models
upsampling
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The 3-D LiDAR scanner and the 2-D charge-coupled device (CCD) camera are two typical types of sensors for surrounding-environment perceiving in robotics or autonomous driving. Commonly, they are jointly used to improve perception accuracy by simultaneously recording the distances of surrounding objects, as well as the color and shape information. In this paper, we use the correspondence between a 3-D LiDAR scanner and a CCD camera to rearrange the captured LiDAR point cloud into a dense depth map, in which each 3-D point corresponds to a pixel at the same location in the RGB image. In this paper, we assume that the LiDAR scanner and the CCD camera are accurately calibrated and synchronized beforehand so that each 3-D LiDAR point cloud is aligned with its corresponding RGB image. Each frame of the LiDAR point cloud is then projected onto the RGB image plane to form a sparse depth map. Then, a self-adaptive method is proposed to upsample the sparse depth map into a dense depth map, in which the RGB image and the anisotropic diffusion tensor are exploited to guide upsampling by reinforcing the RGB-depth compactness. Finally, convex optimization is applied on the dense depth map for global enhancement. Experiments on the KITTI and Middlebury data sets demonstrate that the proposed method outperforms several other relevant state-of-the-art methods in terms of visual comparison and root-mean-square error measurement.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2016.2564640