OrbbecSDK_v2

C++ Sample: 1.lidar_stream

Overview

This sample demonstrates how to configure and start LiDAR and IMU sensor streams, process the data frames through a callback function, and display sensor information.

Knowledge

• Pipeline is a data stream processing pipeline that provides multi-stream configuration, switching, frame aggregation, and frame synchronization functions
• Config is used to configure which streams to enable and their parameters
• FrameSet contains multiple types of sensor data frames
• Callback mode allows asynchronous processing of incoming data

Code Overview

1. Initialize context and device selection

    ob::Context context;
    auto deviceList = context.queryDeviceList();
    auto device = selectDevice(deviceList);
   

2. Initialize pipeline and configure streams

    ob::Pipeline pipe(device);
    std::shared_ptr<ob::Config> config = std::make_shared<ob::Config>();
       
    // Select and enable desired streams
    selectStreams(device, config);
    config->setFrameAggregateOutputMode(OB_FRAME_AGGREGATE_OUTPUT_ALL_TYPE_FRAME_REQUIRE);
   

3. Set/Get property

    // Get property
    device->getStructuredData(OB_RAW_DATA_LIDAR_IP_ADDRESS, data, &dataSize);
    // Set property
    device->setIntProperty(OB_PROP_LIDAR_TAIL_FILTER_LEVEL_INT, 0);
   

4. Start pipeline with callback function for frame processing

    pipe.start(config, [&](std::shared_ptr<ob::FrameSet> frameSet) {
        // Process each frame in the frameset
        for(uint32_t i = 0; i < frameSet->getCount(); i++) {
            auto frame = frameSet->getFrame(i);
               
            // Print frame information every 50 frames
            if(frameCount % 50 == 0) {
                if(frame->getType() == OB_FRAME_LIDAR_POINTS) {
                    auto lidarFrame = frame->as<ob::LiDARPointsFrame>();
                    printLiDARPointCloudInfo(lidarFrame);
                }
                else if(frame->getType() == OB_FRAME_ACCEL) {
                    // Process accelerometer data
                }
                else if(frame->getType() == OB_FRAME_GYRO) {
                    // Process gyroscope data
                }
            }
        }
        frameCount++;
    });
   

5. Stream selection and configuration interface

    void selectStreams(std::shared_ptr<ob::Device> device, std::shared_ptr<ob::Config> config) {
        auto selectedSensors = selectSensors(device);
        for(auto &sensor: selectedSensors) {
            auto streamProfileList = sensor->getStreamProfileList();
            // Enable selected stream profile
            config->enableStream(selectedStreamProfile);
        }
    }
   

6. LiDAR point cloud data processing

    void printLiDARPointCloudInfo(std::shared_ptr<ob::LiDARPointsFrame> pointCloudFrame) {
        auto pointCloudType = pointCloudFrame->getFormat();
        uint32_t validPointCount = 0;
           
        // Process different point cloud formats
        switch(pointCloudType) {
        case OB_FORMAT_LIDAR_SPHERE_POINT:
            // Process 3D point data
            break;
        case OB_FORMAT_LIDAR_POINT:
            // Process 3D point data
            break;
        case OB_FORMAT_LIDAR_SCAN:
            // Process 2D scan data  
            break;
        }
           
        std::cout << "valid point count = " << validPointCount << std::endl;
    }
   

Run Sample

• The program will display available sensors and stream profiles for selection
• Selected streams will start running and print frame information every 50 frames
• Press ESC key to exit the program

Result

The program outputs LiDAR point cloud information (frame index, timestamp, format, valid point count) and IMU data (acceleration, angular velocity) at regular intervals, demonstrating real-time sensor data acquisition and processing.

Device list:
0. name: LiDAR ME450, vid: 0x2bc5, pid: 0x1302, uid: 0x20:4b:5e:00:43:09, sn: T0H6851001Z
1. name: LiDAR ME450, vid: 0x2bc5, pid: 0x1302, uid: 0x20:4b:5e:13:64:30, sn: T0H6851000Z
Select a device: 1

------------------------------------------------------------------------
Current Device:  name: LiDAR ME450, vid: 0x2bc5, pid: 0x1302, uid: 0x20:4b:5e:13:64:30, sn: T0H6851000Z
LiDAR IP Address: 192.168.1.100
Sensor list:
 - 0.sensor type: Accel
 - 1.sensor type: Gyro
 - 2.sensor type: LiDAR
 - 3.all sensors
Select a sensor to enable(input sensor index, '3' to select all sensors):
Stream profile list for sensor: Accel
 - 0.acc rate: 50_HZ
 - 1.acc rate: 25_HZ
 - 2.acc rate: 100_HZ
 - 3.acc rate: 200_HZ
Select a stream profile to enable (input stream profile index):
Stream profile list for sensor: Gyro
 - 0.gyro rate: 50_HZ
 - 1.gyro rate: 25_HZ
 - 2.gyro rate: 100_HZ
 - 3.gyro rate: 200_HZ
Select a stream profile to enable (input stream profile index):
Stream profile list for sensor: LiDAR
 - 0.format: LIDAR_SPHERE_POINT, scan rate: 20HZ
 - 1.format: LIDAR_POINT, scan rate: 20HZ
 - 2.format: LIDAR_SPHERE_POINT, scan rate: 15HZ
 - 3.format: LIDAR_POINT, scan rate: 15HZ
 - 4.format: LIDAR_SPHERE_POINT, scan rate: 10HZ
 - 5.format: LIDAR_POINT, scan rate: 10HZ
Select a stream profile to enable (input stream profile index):
The stream is started!
Press ESC to exit!

frame index: 1
LiDAR PointCloud Frame:
{
  tsp = 1761617624998914
  format = LIDAR_SPHERE_POINT
  valid point count = 6156
}

frame index: 3
Accel Frame:
{
  tsp = 1761617624999927
  temperature = 49.01
  Accel.x = -0.0130615m/s^2
  Accel.y = -0.00805664m/s^2
  Accel.z = -1.00146m/s^2
}

frame index: 3
Gyro Frame:
{
  tsp = 1761617624999927
  temperature = 49.01
  Gyro.x = 1.10626rad/s
  Gyro.y = -0.320435rad/s
  Gyro.z = -0.221252rad/s
}

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