In order to run this example, a device supporting pose stream (T265) is required.


This sample builds on the concepts presented in rs-pose example and shows how pose data can be used asynchronously to implement simple pose prediction.

Expected Output

The application should open a window in which it prints the predicted x, y, z values of the device position relative to its initial position.

Code Overview

We start by configuring the pipeline to pose stream, similar to previous example:

// Declare RealSense pipeline, encapsulating the actual device and sensors
rs2::pipeline pipe;
// Create a configuration for configuring the pipeline with a non default profile
rs2::config cfg;
// Add pose stream
cfg.enable_stream(RS2_STREAM_POSE, RS2_FORMAT_6DOF);

Next, we define new frame callback (learn more about callback API in our GitHub) that would execute for every new pose event from the sensor.

// Define frame callback
// The callback is executed on a sensor thread and can be called simultaneously from multiple sensors
// Therefore any modification to common memory should be done under lock
std::mutex mutex;
auto callback = [&](const rs2::frame& frame)
    std::lock_guard<std::mutex> lock(mutex);
    if (rs2::pose_frame fp =<rs2::pose_frame>()) {
        rs2_pose pose_data = fp.get_pose_data();
        rs2_pose predicted_pose = predict_pose(pose_data, dt_us);
        std::cout << pose_data.tracker_confidence << " : " << std::setprecision(3) << std::fixed << 
                predicted_pose.translation.x << " " <<
                predicted_pose.translation.y << " " <<
                predicted_pose.translation.z << " (meters)\r";

Once pipeline is started with a callback, main thread can go to sleep until termination signal is received:

// Start streaming through the callback with default recommended configuration
rs2::pipeline_profile profiles = pipe.start(cfg, callback);
std::cout << "started thread\n";
while(true) {