Shulou(Shulou.com)06/03 Report--

Lidar

With the development of intelligent driving technology, lidar has quickly entered the field of vision of engineers, whether mechanical, MEMS or pure solid-state lidar. In essence, lidar scans the irradiated area at a certain speed. In this process, lidar constantly sends out laser and receives reflection information. The reflection point information of the lidar includes distance, time and horizontal angle (Azimuth). At the same time, combined with the fixed vertical angle of the laser transmitter, the coordinate information of the reflection point can be calculated, and the set of all reflection points in each scan is the point cloud (Point Cloud). With the explosion of data brought by the high precision of lidar, the real-time processing of point cloud data has become a major challenge for engineers.

At this stage, engineers deal with the calibration, classification and segmentation of point clouds. Using PCL (point cloud database), we can extract point cloud features, deal with target classification, target segmentation and visualization and other requirements. At the same time, when dealing with point cloud data, we also need to synchronously consider the access of other signals, such as video, millimeter wave, all kinds of bus signals and so on.

Therefore, whether there are suitable tools can not only synchronously collect all kinds of data, but also conveniently support engineers to develop, is a problem that intelligent driving development engineers need to face directly.

Real-time processing of point cloud data

ViCANdo Suite is a development and testing platform for intelligent driving functions, which can collect all kinds of data needed synchronously, including but not limited to the data sources in the following list. At the same time, the tool integrates PCL (point cloud library), and engineers can directly use PCL for related development.

Data sources directly supported by ViCANdo Suite:

Example application: LIDAR point cloud data processing

♦ ground point cloud (plane) segmentation, the system uses two 16-line lidar

♦ purpose: there are a considerable number of ground signals in the point clouds detected by LIDAR, which is not conducive to obstacle identification and increases the computational burden.

♦ method: RANSAC (random sampling consistency)

Pcl::SACSegmentation

Example application: obstacle clustering

♦ method: Euclidean clustering (only considering spatial distance or additional conditions such as intensity)

Only consider the distance

Pcl::EuclideanClusterExtraction

Considering distance and strength

Intensitypcl::ConditionalEuclideanClustering

Example application: point cloud / video display and processing interface of ViCANdo

ViCANdo Suite product introduction

ViCANdo Suite is a software suite for the development and testing of intelligent driving functions.

The software contains the following components:

ViCANdo:

ViCANdo is a cross-platform data recording and analysis tool developed for AD/ADAS, which supports operating systems such as Windows, Linux, MacOS, X86, ARM and other platforms.

ViCANlog:

The software designed for vehicle-end road test acquisition can run on any linux-based hardware equipment and collect data unattended. In addition to general computers, it also supports Nvidia TX-2, Xavier,Nexcom and other devices.

ViCANdroid:

APP on Android can collect CAN bus, video, audio, GPS and other data, built-in FTP service, and can automatically upload data to the server. It is convenient to process and analyze the data.

ViCANsim:

Through the integration with OpenCV and Matlab/Simulink ™, it is convenient for users to deploy their own algorithms on ViCANsim, which also has an interface with Matlab/Simulink ™.

ViCANbedded:

The goal of ViCANbedded is to apply in an embedded environment, the same as ViCANdo. It is designed to easily migrate the source code from the PC platform to the embedded environment. Currently, it supports QTEmbedded 4.X and later, QNX, Velocity, VxWorks, and so on.

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