Closed pamzerbhu closed 4 years ago
@xuhao1 have tried mynteye. He can run this repo in real-time on TX2. According to his feedback, there are some issues with mynteye. You can ask him for some advice. Here are some suggestion from me:
@pamzerbhu I think you just have not enable the super power mode sudo nvpmodel -m0 sudo ~/jetson_clock.sh
@xuhao1 have tried mynteye. He can run this repo in real-time on TX2. According to his feedback, there are some issues with mynteye. You can ask him for some advice. Here are some suggestion from me:
- Make sure the data from the device is ok.(Whether the timestamp is sync?)
- Print the feature tracker and estimator time to check which part leads to the delay
- You'd better provide more details about your test.
Thanks for your reply, my mynteye cam is D1000-50/Color,and I set launch with framerate=30 ,resolution=1280*480. when the vins works, CPU single thread has reached 150% It seems that cpu can't deal with so much frame that caused latency?
@pamzerbhu I think you just have not enable the super power mode sudo nvpmodel -m0 sudo ~/jetson_clock.sh
Thanks for your reply, this way has a little help, but with time go on it still out of mem
@pamzerbhu Than reduce the framerate to 20hz and have a try
`%YAML:1.0
common parameters
support: 1 imu 1 cam; 1 imu 2 cam: 2 cam;
imu: 1
num_of_cam: 2
imu_topic: "/mynteye/imu/data_raw" image0_topic: "/mynteye/left/image_raw" image1_topic: "/mynteye/right/image_raw" output_path: "/home/tong/output/"
cam0_calib: "left_mei.yaml" cam1_calib: "right_mei.yaml" image_width: 752 image_height: 480
Extrinsic parameter between IMU and Camera.
estimate_extrinsic: 0 # 0 Have an accurate extrinsic parameters. We will trust the following imu^R_cam, imu^T_cam, don't change it.
1 Have an initial guess about extrinsic parameters. We will optimize around your initial guess.
body_T_cam0: !!opencv-matrix rows: 4 cols: 4 dt: d data: [ 4.2812441490024389e-03, -9.9997001507473682e-01, -6.4528985710044385e-03, 5.2583356071589790e-05, 9.9996900935734523e-01, 4.2384270612576547e-03, 6.6344601088757426e-03, -4.2174706544162562e-02, -6.6069110351583190e-03, -6.4811023350536514e-03, 9.9995717110239080e-01, 1.9238715201769417e-02, 0., 0., 0., 1. ]
body_T_cam1: !!opencv-matrix rows: 4 cols: 4 dt: d data: [ 1.6525177162010074e-03, -9.9998509853672479e-01, -5.2030654132755290e-03, -2.9251875934051923e-03, 9.9994935393851081e-01, 1.6007594247438028e-03, 9.9361525360214843e-03, 7.4379061731550719e-02, -9.9276756168115085e-03, -5.2192215666013279e-03, 9.9993709851324453e-01, 1.9302672329335892e-02, 0., 0., 0., 1. ]
Multiple thread support
multiple_thread: 1
Gpu accleration support
use_gpu: 1 use_gpu_acc_flow: 1
feature traker paprameters
max_cnt: 150 # max feature number in feature tracking min_dist: 30 # min distance between two features freq: 10 # frequence (Hz) of publish tracking result. At least 10Hz for good estimation. If set 0, the frequence will be same as raw image F_threshold: 1.0 # ransac threshold (pixel) show_track: 1 # publish tracking image as topic flow_back: 1 # perform forward and backward optical flow to improve feature tracking accuracy
optimization parameters
max_solver_time: 0.04 # max solver itration time (ms), to guarantee real time max_num_iterations: 8 # max solver itrations, to guarantee real time keyframe_parallax: 10.0 # keyframe selection threshold (pixel)
imu parameters The more accurate parameters you provide, the better performance
acc_n: 0.01 # accelerometer measurement noise standard deviation. #0.2 0.04 gyr_n: 0.001 # gyroscope measurement noise standard deviation. #0.05 0.004 acc_w: 0.0001 # accelerometer bias random work noise standard deviation. #0.02 gyr_w: 0.00001 # gyroscope bias random work noise standard deviation. #4.0e-5 g_norm: 9.8 # gravity magnitude
unsynchronization parameters
estimate_td: 0 # online estimate time offset between camera and imu td: 0.0 # initial value of time offset. unit: s. readed image clock + td = real image clock (IMU clock)
loop closure parameters
load_previous_pose_graph: 0 # load and reuse previous pose graph; load from 'pose_graph_save_path' pose_graph_save_path: "/home/tony-ws1/output/pose_graph/" # save and load path save_image: 1 # save image in pose graph for visualization prupose; you can close this function by setting 0 ` 这是我的配置文件,开启了GPU加速但是似乎没有解决延迟问题,而且随着时间增长内存会爆掉。