Code is intended for the Waveshare High-Precision AD HAT
It is adopted from the manufacturer's repo
I have added passive waiting for the ready signal. The original code implements active waiting which means that one core is always at 100% load. With the pasive waiting I ran read 7200 SPS at around 24% load of a single core.
| FROM ghcr.io/cloudnative-pg/postgresql:16.6-11-bullseye@sha256:dc7bd9127c1b7055760a1201efda181ef16903326597935796591cbc5e13a73b | |
| USER root | |
| RUN apt-get update \ | |
| && apt-get install -y --no-install-recommends \ | |
| postgresql-server-dev-16 \ | |
| build-essential \ | |
| autoconf flex git libcurl4-gnutls-dev libicu-dev \ | |
| libkrb5-dev liblz4-dev libpam0g-dev libreadline-dev \ |
| import datetime | |
| from urllib.parse import urlparse | |
| def linkedin_post_datetime(url: str) -> datetime.datetime: | |
| """ | |
| Convert LinkedIN post URL to datetime of when the post was created. | |
| You can gen the post URL by clicking tripple dots in upper right corner of the post | |
| and then selecting "Copy link to post". |
| (env) tomaskrejci @ Tomass-MBP-2.RD ➜ pymsckf git:(replay-from-app) ✗ docker run -v ~/ios_recordings:/data -v ~/Developer/pymsckf/examples:/examples -v `pwd`/calibration_output:/output --security-opt seccomp:unconfined -it -e DISPLAY=docker.for.mac.localhost:0 -w /output pymsckf_calibration kalibr_calibrate_imu_camera --bag /data/ios_recording_101.bag --cam /output/camchain-dataios_recording_96.yaml --imu /examples/imu_vins_mobile.yaml --target /examples/april_6x6_50x50cm.yaml | |
| importing libraries | |
| Initializing IMUs: | |
| Update rate: 100.0 | |
| Accelerometer: | |
| Noise density: 0.02 | |
| Noise density (discrete): 0.2 | |
| Random walk: 0.002 | |
| Gyroscope: | |
| Noise density: 0.02 |
| (env) tomaskrejci @ Tomass-MBP-2.RD ➜ pymsckf git:(replay-from-app) ✗ docker run -v ~/ios_recordings:/data -v ~/Developer/pymsckf/examples:/examples -v `pwd`/calibration_output:/output --security-opt seccomp:unconfined -it -e DISPLAY=docker.for.mac.localhost:0 -w /output pymsckf_calibration kalibr_calibrate_imu_camera --bag /data/ios_recording_100.bag --cam /output/camchain-dataios_recording_96.yaml --imu /examples/imu_vins_mobile.yaml --target /examples/april_6x6_50x50cm.yaml | |
| importing libraries | |
| Initializing IMUs: | |
| Update rate: 100.0 | |
| Accelerometer: | |
| Noise density: 0.02 | |
| Noise density (discrete): 0.2 | |
| Random walk: 0.002 | |
| Gyroscope: | |
| Noise density: 0.02 |
| cam0: | |
| T_cam_imu: | |
| - [0.005397773388641874, -0.9998572413908012, 0.016011273554103707, 0.09315162124446877] | |
| - [-0.999983932903204, -0.0054247971439093275, -0.0016448438801904336, 0.016759819073606755] | |
| - [0.0017314669756124, -0.016002137804896704, -0.9998704584133825, -0.007388480810763209] | |
| - [0.0, 0.0, 0.0, 1.0] | |
| cam_overlaps: [] | |
| camera_model: pinhole | |
| distortion_coeffs: [0.03745064619036303, -0.05696336726790861, 0.0014705393416119647, | |
| 0.003161343303012978] |
| Calibration results | |
| ==================== | |
| Camera-system parameters: | |
| cam0 (/cam0/image_raw): | |
| type: <class 'aslam_cv.libaslam_cv_python.DistortedPinholeCameraGeometry'> | |
| distortion: [ 0.03745065 -0.05696337 0.00147054 0.00316134] +- [ 0.00352425 0.00648838 0.00070408 0.00066755] | |
| projection: [ 524.58054214 525.4382302 327.07092587 229.298555 ] +- [ 1.3060177 1.25092538 1.07929177 1.27724979] | |
| reprojection error: [-0.000002, 0.000002] +- [0.229364, 0.214071] | |
| pymsckf convert_kalibr -d ~/Downloads/ios_recording_96.sqlite -o ~/ios_recordings/ios_recording_96 |
| #Accelerometers | |
| accelerometer_noise_density: 0.02 #Noise density (continuous-time) | |
| accelerometer_random_walk: 0.002 #Bias random walk | |
| #Gyroscopes | |
| gyroscope_noise_density: 0.02 #Noise density (continuous-time) | |
| gyroscope_random_walk: 4.0e-5 #Bias random walk | |
| rostopic: /imu0 #the IMU ROS topic | |
| update_rate: 100.0 #Hz (for discretization of the values above) |