Advanced driver assistance systems use 3-D point clouds obtained from lidar scans to measure physical surfaces. Lidar point cloud processing enables you to downsample, denoise, and transform these point clouds before registering them or segmenting them into clusters. You can also read, write, store, display, and compare point clouds, including point clouds imported from Velodyne packet capture (PCAP) files.
You can also detect road lanes in lidar point clouds using a deep learning approach. Automated Driving Toolbox™ provides a lidar lane detection network trained on the K-Lane data set. The pretrained network enables you to detect a maximum of six lanes. You can also evaluate the performance of detector using different metrics, such as classification accuracy, precision, recall, and F1-score.
To detect lanes in lidar point clouds, download the Automated Driving Toolbox Model for Lidar Lane Detection support package from the Add-On Explorer. For more information on downloading add-ons, see Get and Manage Add-Ons.
I/O and Storage
|Object for storing 3-D point cloud|
|Read point cloud data from Velodyne PCAP file|
|Read 3-D point cloud from PLY or PCD file|
|Write 3-D point cloud to PLY or PCD file|
|Read message headers from Ibeo Data Container (IDC) file (Since R2021a)|
|Select subset of messages to read from Ibeo Data Container (IDC) file (Since R2021a)|
|Object for reading message content from Ibeo Data Container (IDC) file (Since R2021a)|
|Read messages from Ibeo Data Container (IDC) file selection (Since R2021a)|
- Get Started with Lidar Lane Detection Using Deep Learning
Use lidar lane detection network to detect road lanes.
- Detect, Classify, and Track Vehicles Using Lidar (Lidar Toolbox)
Detect, classify, and track vehicles by using lidar point cloud data captured by a lidar sensor mounted on an ego vehicle.