Running on Jetson Nano? Maxwell 128 cores?

[PaulKrush]

Cool Repo! Do you think VINS-Fusion will run well on a Jetson Nano?

[pjrambo]

Thanks! Haven't gotten a Jetson Nano yet :(

[ghost]

Hi ! I am testing Jetson Nano.But I have encountered some problems,I manually installed opencv 3.4.5 with cuda,I compiled VINS-Fusion-gpu and I received some warnings.

[  1%] Linking CXX shared library /home/jetsonnano/catkin_ws/VINS-Fusion/devel/lib/libcamera_models.so
[ 19%] Built target camera_models
[ 39%] Built target Calibrations
[ 41%] Linking CXX shared library /home/jetsonnano/catkin_ws/VINS-Fusion/devel/lib/liblibGeographiccc.so
[ 45%] Built target libGeographiccc
[ 46%] Linking CXX executable /home/jetsonnano/catkin_ws/VINS-Fusion/devel/lib/global_fusion/global_fusion_node
[ 49%] Built target global_fusion_node
[ 50%] Linking CXX executable /home/jetsonnano/catkin_ws/VINS-Fusion/devel/lib/loop_fusion/loop_fusion_node
/usr/bin/ld: warning: libopencv_imgproc.so.3.4, needed by /home/jetsonnano/catkin_ws/VINS-Fusion/devel/lib/libcamera_models.so, may conflict with libopencv_imgproc.so.3.2
/usr/bin/ld: warning: libopencv_core.so.3.4, needed by /home/jetsonnano/catkin_ws/VINS-Fusion/devel/lib/libcamera_models.so, may conflict with libopencv_core.so.3.2
/usr/bin/ld: warning: libopencv_imgcodecs.so.3.4, needed by /home/jetsonnano/OpenCV/opencv/build/lib/libopencv_sfm.so.3.4.5, may conflict with libopencv_imgcodecs.so.3.2
[ 68%] Built target loop_fusion_node
[ 69%] Linking CXX shared library /home/jetsonnano/catkin_ws/VINS-Fusion/devel/lib/libvins_lib.so
[ 91%] Built target vins_lib
[ 93%] Linking CXX executable /home/jetsonnano/catkin_ws/VINS-Fusion/devel/lib/vins/kitti_odom_test
/usr/bin/ld: warning: libopencv_core.so.3.4, needed by /home/jetsonnano/catkin_ws/VINS-Fusion/devel/lib/libvins_lib.so, may conflict with libopencv_core.so.3.2
/usr/bin/ld: warning: libopencv_imgcodecs.so.3.4, needed by /home/jetsonnano/OpenCV/opencv/build/lib/libopencv_sfm.so.3.4.5, may conflict with libopencv_imgcodecs.so.3.2
[ 94%] Built target kitti_odom_test
[ 95%] Linking CXX executable /home/jetsonnano/catkin_ws/VINS-Fusion/devel/lib/vins/vins_node
/usr/bin/ld: warning: libopencv_core.so.3.4, needed by /home/jetsonnano/catkin_ws/VINS-Fusion/devel/lib/libvins_lib.so, may conflict with libopencv_core.so.3.2
[ 97%] Built target vins_node
[ 98%] Linking CXX executable /home/jetsonnano/catkin_ws/VINS-Fusion/devel/lib/vins/kitti_gps_test
/usr/bin/ld: warning: libopencv_core.so.3.4, needed by /home/jetsonnano/catkin_ws/VINS-Fusion/devel/lib/libvins_lib.so, may conflict with libopencv_core.so.3.2
/usr/bin/ld: warning: libopencv_imgcodecs.so.3.4, needed by /home/jetsonnano/OpenCV/opencv/build/lib/libopencv_sfm.so.3.4.5, may conflict with libopencv_imgcodecs.so.3.2
[100%] Built target kitti_gps_test

So when I run this package, I get an error saying that my opencv does not have cuda support.

[ghost]

The problem has been solved, this is cv_bridge and opencv compatibility issues

[FrozenBurning]

The problem has been solved, this is cv_bridge and opencv compatibility issues

Could you tell me how you solve this problem? I face the same one, successful compiled but the package throws an error that the opencv in my ros does not have cuda support. But I DO have opencv in cuda version. Thanks!

[pjrambo]

The problem has been solved, this is cv_bridge and opencv compatibility issues

Could you tell me how you solve this problem? I face the same one, successful compiled but the package throws an error that the opencv in my ros does not have cuda support. But I DO have opencv in cuda version. Thanks!

You need to find the cv_bridge source code from https://github.com/ros-perception/vision_opencv and clone it to your ros workspace. Then modify the cv_bridge CMakelist. Let the cv_bridge find your CUDA version opencv. Finally, catkin_make, problem solved

[FrozenBurning]

The problem has been solved, this is cv_bridge and opencv compatibility issues

Could you tell me how you solve this problem? I face the same one, successful compiled but the package throws an error that the opencv in my ros does not have cuda support. But I DO have opencv in cuda version. Thanks!

You need to find the cv_bridge source code from https://github.com/ros-perception/vision_opencv and clone it to your ros workspace. Then modify the cv_bridge CMakelist. Let the cv_bridge find your CUDA version opencv. Finally, catkin_make, problem solved

Gotcha! Thanks

[PaulKrush]

This is great information! I should just go buy a Nano and try it myself.

@YuyingJin0111 Do you have it running? Does it work well. What kind of resources does it use? CPU cores and %? GPU%? Memory? Thanks!

[ghost]

@PaulKrush On the euroc data, my measured performance is not good. There is a big delay, I have not solved this problem. But I implemented GPU acceleration on Vins-Mono and had a better real-time performance.

[pjrambo]

@PaulKrush On the euroc data, my measured performance is not good. There is a big delay, I have not solved this problem. But I implemented GPU acceleration on Vins-Mono and had a better real-time performance.

Have you set your board to the max performance? Could you please show your cpu usage rate and gpu usage rate?

[ghost]

This is the result of my current test. I am still a newcomer to the jetson platform. I don't know how to look at the usage rate of the gpu.

Sudo: ~/tegrastats: command not found

[ghost]

@pjrambo

Euroc bag is over.

[struggleforbetter]

I also encountered this problem.on my computer, not using GPU acceleration, but faster.I track the image both with and not with GPU acceleration simultaneously.this is code `map<int, vector<pair<int, Eigen::Matrix<double, 7, 1>>>> FeatureTracker::trackImage(double _cur_time, const cv::Mat &_img, const cv::Mat &_img1) { static int count = 0; count ++; double sum_spend_time = 0.0; static double norm_spend_time = 0.0; static double gpu_spend_time = 0.0; TicToc t_r; cur_time = _cur_time; cur_img = _img; row = cur_img.rows; col = cur_img.cols; cv::Mat rightImg = _img1; / { cv::Ptr clahe = cv::createCLAHE(3.0, cv::Size(8, 8)); clahe->apply(cur_img, cur_img); if(!rightImg.empty()) clahe->apply(rightImg, rightImg); } / cur_pts.clear();

if (prev_pts.size() > 0)
{
    vector<uchar> status;
         // 不使用GPU
        TicToc t_o;

        vector<float> err;
        if(hasPrediction)
        {
            cur_pts = predict_pts;
            cv::calcOpticalFlowPyrLK(prev_img, cur_img, prev_pts, cur_pts, status, err, cv::Size(21, 21), 1, 
            cv::TermCriteria(cv::TermCriteria::COUNT+cv::TermCriteria::EPS, 30, 0.01), cv::OPTFLOW_USE_INITIAL_FLOW);

            int succ_num = 0;
            for (size_t i = 0; i < status.size(); i++)
            {
                if (status[i])
                    succ_num++;
            }
            if (succ_num < 10)
            cv::calcOpticalFlowPyrLK(prev_img, cur_img, prev_pts, cur_pts, status, err, cv::Size(21, 21), 3);
        }
        else
            cv::calcOpticalFlowPyrLK(prev_img, cur_img, prev_pts, cur_pts, status, err, cv::Size(21, 21), 3);
        // reverse check
        if(FLOW_BACK)
        {
            vector<uchar> reverse_status;
            vector<cv::Point2f> reverse_pts = prev_pts;
            cv::calcOpticalFlowPyrLK(cur_img, prev_img, cur_pts, reverse_pts, reverse_status, err, cv::Size(21, 21), 1, 
            cv::TermCriteria(cv::TermCriteria::COUNT+cv::TermCriteria::EPS, 30, 0.01), cv::OPTFLOW_USE_INITIAL_FLOW);
            //cv::calcOpticalFlowPyrLK(cur_img, prev_img, cur_pts, reverse_pts, reverse_status, err, cv::Size(21, 21), 3); 
            for(size_t i = 0; i < status.size(); i++)
            {
                if(status[i] && reverse_status[i] && distance(prev_pts[i], reverse_pts[i]) <= 0.5)
                {
                    status[i] = 1;
                }
                else
                    status[i] = 0;
            }
        }
        // printf("temporal optical flow costs: %fms\n", t_o.toc());
        norm_spend_time += t_o.toc();
        // 使用GPU
        cur_pts.clear();
        TicToc t_og;
        cv::cuda::GpuMat prev_gpu_img(prev_img);
        cv::cuda::GpuMat cur_gpu_img(cur_img);
        cv::cuda::GpuMat prev_gpu_pts(prev_pts);
        cv::cuda::GpuMat cur_gpu_pts(cur_pts);
        cv::cuda::GpuMat gpu_status;
        if(hasPrediction)
        {
            cur_gpu_pts = cv::cuda::GpuMat(predict_pts);
            cv::Ptr<cv::cuda::SparsePyrLKOpticalFlow> d_pyrLK_sparse = cv::cuda::SparsePyrLKOpticalFlow::create(
            cv::Size(21, 21), 1, 30, true);
            d_pyrLK_sparse->calc(prev_gpu_img, cur_gpu_img, prev_gpu_pts, cur_gpu_pts, gpu_status);

            vector<cv::Point2f> tmp_cur_pts(cur_gpu_pts.cols);
            cur_gpu_pts.download(tmp_cur_pts);
            cur_pts = tmp_cur_pts;

            vector<uchar> tmp_status(gpu_status.cols);
            gpu_status.download(tmp_status);
            status = tmp_status;

            int succ_num = 0;
            for (size_t i = 0; i < tmp_status.size(); i++)
            {
                if (tmp_status[i])
                    succ_num++;
            }
            if (succ_num < 10)
            {
                cv::Ptr<cv::cuda::SparsePyrLKOpticalFlow> d_pyrLK_sparse = cv::cuda::SparsePyrLKOpticalFlow::create(
                cv::Size(21, 21), 3, 30, false);
                d_pyrLK_sparse->calc(prev_gpu_img, cur_gpu_img, prev_gpu_pts, cur_gpu_pts, gpu_status);

                vector<cv::Point2f> tmp1_cur_pts(cur_gpu_pts.cols);
                cur_gpu_pts.download(tmp1_cur_pts);
                cur_pts = tmp1_cur_pts;

                vector<uchar> tmp1_status(gpu_status.cols);
                gpu_status.download(tmp1_status);
                status = tmp1_status;
            }
        }
        else
        {
            cv::Ptr<cv::cuda::SparsePyrLKOpticalFlow> d_pyrLK_sparse = cv::cuda::SparsePyrLKOpticalFlow::create(
            cv::Size(21, 21), 3, 30, false);
            d_pyrLK_sparse->calc(prev_gpu_img, cur_gpu_img, prev_gpu_pts, cur_gpu_pts, gpu_status);

            vector<cv::Point2f> tmp1_cur_pts(cur_gpu_pts.cols);
            cur_gpu_pts.download(tmp1_cur_pts);
            cur_pts = tmp1_cur_pts;

            vector<uchar> tmp1_status(gpu_status.cols);
            gpu_status.download(tmp1_status);
            status = tmp1_status;
        }
        if(FLOW_BACK)
        {
            cv::cuda::GpuMat reverse_gpu_status;
            cv::cuda::GpuMat reverse_gpu_pts = prev_gpu_pts;
            cv::Ptr<cv::cuda::SparsePyrLKOpticalFlow> d_pyrLK_sparse = cv::cuda::SparsePyrLKOpticalFlow::create(
            cv::Size(21, 21), 1, 30, true);
            d_pyrLK_sparse->calc(cur_gpu_img, prev_gpu_img, cur_gpu_pts, reverse_gpu_pts, reverse_gpu_status);

            vector<cv::Point2f> reverse_pts(reverse_gpu_pts.cols);
            reverse_gpu_pts.download(reverse_pts);

            vector<uchar> reverse_status(reverse_gpu_status.cols);
            reverse_gpu_status.download(reverse_status);

            for(size_t i = 0; i < status.size(); i++)
            {
                if(status[i] && reverse_status[i] && distance(prev_pts[i], reverse_pts[i]) <= 0.5)
                {
                    status[i] = 1;
                }
                else
                    status[i] = 0;
            }
        }
        // printf("gpu temporal optical flow costs: %f ms\n",t_og.toc());
        gpu_spend_time += t_og.toc();

    for (int i = 0; i < int(cur_pts.size()); i++)
        if (status[i] && !inBorder(cur_pts[i]))
            status[i] = 0;
    reduceVector(prev_pts, status);
    reduceVector(cur_pts, status);
    reduceVector(ids, status);
    reduceVector(track_cnt, status);
    // ROS_DEBUG("temporal optical flow costs: %fms", t_o.toc());

    //printf("track cnt %d\n", (int)ids.size());
}

for (auto &n : track_cnt)
    n++;

if (1)
{
    //rejectWithF();
    ROS_DEBUG("set mask begins");
    TicToc t_m;
    setMask();
    // ROS_DEBUG("set mask costs %fms", t_m.toc());
    // printf("set mask costs %fms\n", t_m.toc());
    ROS_DEBUG("detect feature begins");

    int n_max_cnt = MAX_CNT - static_cast<int>(cur_pts.size());
    // if(!USE_GPU)
    {
        TicToc t_t;
        if (n_max_cnt > 0)
        {

            if(mask.empty())
                cout << "mask is empty " << endl;
            if (mask.type() != CV_8UC1)
                cout << "mask type wrong " << endl;
            cv::goodFeaturesToTrack(cur_img, n_pts, MAX_CNT - cur_pts.size(), 0.01, MIN_DIST, mask);
            // printf("good feature to track costs: %fms\n", t_t.toc());
            std::cout << "n_pts size: "<< n_pts.size()<<std::endl;
        }
        else
            n_pts.clear();
        // sum_n += n_pts.size();
        // printf("total point from non-gpu: %d\n",sum_n);
        // printf("detect feature costs no GPU: %fms", t_t.toc());
        norm_spend_time += t_t.toc();
    }

    // ROS_DEBUG("detect feature costs: %fms", t_t.toc());
    // printf("good feature to track costs: %fms\n", t_t.toc());
    // else
    {
        TicToc t_g;
        if (n_max_cnt > 0)
        {
            if(mask.empty())
                cout << "mask is empty " << endl;
            if (mask.type() != CV_8UC1)
                cout << "mask type wrong " << endl;

            cv::cuda::GpuMat cur_gpu_img(cur_img);
            cv::cuda::GpuMat d_prevPts;
            TicToc t_gg;
            cv::cuda::GpuMat gpu_mask(mask);
            // printf("gpumat cost: %fms\n",t_gg.toc());
            cv::Ptr<cv::cuda::CornersDetector> detector = cv::cuda::createGoodFeaturesToTrackDetector(cur_gpu_img.type(), MAX_CNT - cur_pts.size(), 0.01, MIN_DIST);
            // cout << "new gpu points: "<< MAX_CNT - cur_pts.size()<<endl;
            detector->detect(cur_gpu_img, d_prevPts, gpu_mask);
            // std::cout << "d_prevPts size: "<< d_prevPts.size()<<std::endl;
            if(!d_prevPts.empty())
                n_pts = cv::Mat_<cv::Point2f>(cv::Mat(d_prevPts));
            else
                n_pts.clear();
            // sum_n += n_pts.size();
            // printf("total point from gpu: %d\n",sum_n);

        }
        else 
            n_pts.clear();
        // printf("gpu good feature to track cost: %fms\n", t_g.toc());
        gpu_spend_time += t_g.toc();
    }

    ROS_DEBUG("add feature begins");
    TicToc t_a;
    addPoints();
    // ROS_DEBUG("selectFeature costs: %fms", t_a.toc());
    // printf("selectFeature costs: %fms\n", t_a.toc());
}

cur_un_pts = undistortedPts(cur_pts, m_camera[0]);
pts_velocity = ptsVelocity(ids, cur_un_pts, cur_un_pts_map, prev_un_pts_map);

if(!_img1.empty() && stereo_cam)
{
    ids_right.clear();
    cur_right_pts.clear();
    cur_un_right_pts.clear();
    right_pts_velocity.clear();
    cur_un_right_pts_map.clear();
    if(!cur_pts.empty())
    {
        //printf("stereo image; track feature on right image\n");

        vector<cv::Point2f> reverseLeftPts;
        vector<uchar> status, statusRightLeft;
        // if(!USE_GPU_ACC_FLOW)
        {
            TicToc t_check;
            vector<float> err;
            // cur left ---- cur right
            cv::calcOpticalFlowPyrLK(cur_img, rightImg, cur_pts, cur_right_pts, status, err, cv::Size(21, 21), 3);
            // reverse check cur right ---- cur left
            if(FLOW_BACK)
            {
                cv::calcOpticalFlowPyrLK(rightImg, cur_img, cur_right_pts, reverseLeftPts, statusRightLeft, err, cv::Size(21, 21), 3);
                for(size_t i = 0; i < status.size(); i++)
                {
                    if(status[i] && statusRightLeft[i] && inBorder(cur_right_pts[i]) && distance(cur_pts[i], reverseLeftPts[i]) <= 0.5)
                        status[i] = 1;
                    else
                        status[i] = 0;
                }
            }
            // printf("left right optical flow cost %fms\n",t_check.toc());
            norm_spend_time += t_check.toc();
        }
        // else
        {
            TicToc t_og1;
            cv::cuda::GpuMat cur_gpu_img(cur_img);
            cv::cuda::GpuMat right_gpu_Img(rightImg);
            cv::cuda::GpuMat cur_gpu_pts(cur_pts);
            cv::cuda::GpuMat cur_right_gpu_pts;
            cv::cuda::GpuMat gpu_status;
            cv::Ptr<cv::cuda::SparsePyrLKOpticalFlow> d_pyrLK_sparse = cv::cuda::SparsePyrLKOpticalFlow::create(
            cv::Size(21, 21), 3, 30, false);
            d_pyrLK_sparse->calc(cur_gpu_img, right_gpu_Img, cur_gpu_pts, cur_right_gpu_pts, gpu_status);

            vector<cv::Point2f> tmp_cur_right_pts(cur_right_gpu_pts.cols);
            cur_right_gpu_pts.download(tmp_cur_right_pts);
            cur_right_pts = tmp_cur_right_pts;

            vector<uchar> tmp_status(gpu_status.cols);
            gpu_status.download(tmp_status);
            status = tmp_status;

            if(FLOW_BACK)
            {   
                cv::cuda::GpuMat reverseLeft_gpu_Pts;
                cv::cuda::GpuMat status_gpu_RightLeft;
                cv::Ptr<cv::cuda::SparsePyrLKOpticalFlow> d_pyrLK_sparse = cv::cuda::SparsePyrLKOpticalFlow::create(
                cv::Size(21, 21), 3, 30, false);
                d_pyrLK_sparse->calc(right_gpu_Img, cur_gpu_img, cur_right_gpu_pts, reverseLeft_gpu_Pts, status_gpu_RightLeft);

                vector<cv::Point2f> tmp_reverseLeft_Pts(reverseLeft_gpu_Pts.cols);
                reverseLeft_gpu_Pts.download(tmp_reverseLeft_Pts);
                reverseLeftPts = tmp_reverseLeft_Pts;

                vector<uchar> tmp1_status(status_gpu_RightLeft.cols);
                status_gpu_RightLeft.download(tmp1_status);
                statusRightLeft = tmp1_status;
                for(size_t i = 0; i < status.size(); i++)
                {
                    if(status[i] && statusRightLeft[i] && inBorder(cur_right_pts[i]) && distance(cur_pts[i], reverseLeftPts[i]) <= 0.5)
                        status[i] = 1;
                    else
                        status[i] = 0;
                }
            }
            // printf("gpu left right optical flow cost %fms\n",t_og1.toc());
            gpu_spend_time += t_og1.toc();
        }
        ids_right = ids;
        reduceVector(cur_right_pts, status);
        reduceVector(ids_right, status);
        // only keep left-right pts
        /*
        reduceVector(cur_pts, status);
        reduceVector(ids, status);
        reduceVector(track_cnt, status);
        reduceVector(cur_un_pts, status);
        reduceVector(pts_velocity, status);
        */
        cur_un_right_pts = undistortedPts(cur_right_pts, m_camera[1]);
        right_pts_velocity = ptsVelocity(ids_right, cur_un_right_pts, cur_un_right_pts_map, prev_un_right_pts_map);

    }
    prev_un_right_pts_map = cur_un_right_pts_map;
}
if(SHOW_TRACK)
    drawTrack(cur_img, rightImg, ids, cur_pts, cur_right_pts, prevLeftPtsMap);

prev_img = cur_img;
prev_pts = cur_pts;
prev_un_pts = cur_un_pts;
prev_un_pts_map = cur_un_pts_map;
prev_time = cur_time;
hasPrediction = false;

prevLeftPtsMap.clear();
for(size_t i = 0; i < cur_pts.size(); i++)
    prevLeftPtsMap[ids[i]] = cur_pts[i];

map<int, vector<pair<int, Eigen::Matrix<double, 7, 1>>>> featureFrame;
for (size_t i = 0; i < ids.size(); i++)
{
    int feature_id = ids[i];
    double x, y ,z;
    x = cur_un_pts[i].x;
    y = cur_un_pts[i].y;
    z = 1;
    double p_u, p_v;
    p_u = cur_pts[i].x;
    p_v = cur_pts[i].y;
    int camera_id = 0;
    double velocity_x, velocity_y;
    velocity_x = pts_velocity[i].x;
    velocity_y = pts_velocity[i].y;

    Eigen::Matrix<double, 7, 1> xyz_uv_velocity;
    xyz_uv_velocity << x, y, z, p_u, p_v, velocity_x, velocity_y;
    featureFrame[feature_id].emplace_back(camera_id,  xyz_uv_velocity);
}

if (!_img1.empty() && stereo_cam)
{
    for (size_t i = 0; i < ids_right.size(); i++)
    {
        int feature_id = ids_right[i];
        double x, y ,z;
        x = cur_un_right_pts[i].x;
        y = cur_un_right_pts[i].y;
        z = 1;
        double p_u, p_v;
        p_u = cur_right_pts[i].x;
        p_v = cur_right_pts[i].y;
        int camera_id = 1;
        double velocity_x, velocity_y;
        velocity_x = right_pts_velocity[i].x;
        velocity_y = right_pts_velocity[i].y;

        Eigen::Matrix<double, 7, 1> xyz_uv_velocity;
        xyz_uv_velocity << x, y, z, p_u, p_v, velocity_x, velocity_y;
        featureFrame[feature_id].emplace_back(camera_id,  xyz_uv_velocity);
    }
}

//printf("feature track whole time %f\n", t_r.toc());
sum_spend_time += t_r.toc();
// ROS_WARN("feature track average spend time: %f",sum_spend_time/count);
printf("norm average spend time: %f\n",norm_spend_time/count);
printf("gpu average spend time: %f\n",gpu_spend_time/count);
return featureFrame;

} ` when I dont run vins_fusion,gpu is then ,run

[struggleforbetter]

Turning on high performance on TX2 can achieve real-time, but I compared the GoodFeaturesToTrack of CPU and GPU and found that the feature points extracted by CPU are generally more than GPU. Does anyone compare this? @pjrambo

[PaulKrush]

@pjrambo, Sorry, I have yet to try VINS-Fusion-gpu on the Jetson Nano.

[arjunskumar]

Hi, I managed to installed vins-fusion-gpu on Nvidia Jetson Nano, I have documented my steps here vins-fusion-jetson-nano. I was able to run bag files smoothly. Tried with real sense D435i, was able to get good results.

[zrd1234]

@PaulKrush On the euroc data, my measured performance is not good. There is a big delay, I have not solved this problem. But I implemented GPU acceleration on Vins-Mono and had a better real-time performance.

Hi, thank you for your work, I put GPU acceleration of Vins-Fusion-GPU on Vins-Mono. However, the following error occurred when running. Do you know how to solve it?