The APM board can be seen as an interface to the peripheral devices installed in the sailing boat. It is connect via USB to the Jetson Nano and communication is handled via the MAVLink protocol.
This way the servo motors for the rudder and the sail can be set manually in MANUAL mode or set automatically by internal control loops which can be controlled by external parameters in AUTOMATIC mode.
The Jetson Nano acts as mastermind that gives sailing instructions to the APM board. The final goal of the project is, that the Jetson Nano is capable of:
- Detecting a specific object via the camera
- Sending the updated route instructions to the APM board which fires the motor servos.
- Wind sensor that delivers readings relative to the direction of the sail
- Motor servos for controlling the rudder and the sail
- Camera for detecting objects on the water surface, to be used for controlling the sailing course
HBV-1780-2 Camera board with two OV9732 sensors and 72° field of view. Uses only one USB output (UVC protocol), which provides both video frames horizontally joined.
Product on AliExpress Image Sensor
The current APM project is configured to use a baudrate of 115200.
Note: you need to supply external supply voltage to the servos connected to the output pins.
- 6: Wind Sensor
- 1: Rudder
- 3: Sail
- The RC channel columns 1, 2, 3 of the RC receiver have to be connected to the APM inputs 1, 2, 3.
- Connect GND and VCC from the BAT column of the RC receiver with GND and VCC of the APM board.
- The rows of the RC receiver consist of GND, VCC and SIGNAL, where GND is at the edge of the RC receiver board.
Here, it is expected that you are provided with a Jetson Nano as we left it behind in the summer of 2022.
It should be running:
- Ubuntu 20.04
- ROS Noetic Ninjemys
If this is not the case, please follow the Getting started from scratch instructions.
To initially check if the Jetson Nano is running as expected, we suggest to boot it up with a screen, keyboard and mouse connected.
The login details are:
user: nano
password: jetson
In order to be able to connect to the Jetson via SSH, you need to be connected to a non-restricted network unlike eduroam, which blocks SSH connections by default.
When working with the Jetson at TUHH, we used the following setup:
- A Wi-Fi connection to the TP-Link mini access point
(SSID: TP-Link_9C4A, password: 94588637) - A LAN connection to eduroam
Now, you should be able to SSH to the Jetson via the TP-Link access point, while having internet access via eduroam.
You can look up the IP address of the Wi-Fi interface by running ip a
The current project uses the Robot Operating System (ROS) framework.
Here, functionality is encapsulated in so-called nodes which communicate with each other via topics. Nodes can either send, i.e. publish, messages to topics or receive messages from topics by subscribing to them.
In order to have access to ROS commands, the following files should be sourced:
$ source /opt/ros/noetic/setup.bash # The global ROS installation
$ source ~/gitlab_repo/jetson_nano/catkin_ws/devel/setup.bash # The Catkin workspace where all functionality is implemented
$ source ~/gitlab_repo/jetson_nano/catkin_ws_mavros/devel_isolated/setup.bash # The Catkin workspace which is solely used for a custom MAVROS/MAVLINK versionSince these lines are at the end of the ~/.bashrc file, it should happen automatically for every shell session.
Communication with the APM board is handled via MAVROS, the ROS implementation of the MAVLink protocol.
First, a single instance of the MAVROS master node must be launched. It offers a set of topics and services which can be used by other nodes to communicate the APM. NOTE: Since this calibrates the Inertial navigation system (INS), the boat must stand straight and still.
Launch it by running:
$ roslaunch mavros apm.launch fcu_url:=serial:///dev/ttyACM0:115200 fcu_protocol:=v1.0or via our custom launch script:
$ roslaunch mavros master.launchAt the end of the console output, you should see Ready to drive.
Attachment - APM ready to drive
The next step is to calibrate the wind sensor and the motor servos for the rudder and sail.
Launch the calibration node by running
$ rosrun boat_control calibrateThe output of the calibration is a .json file at /home/nano/gitlab_repo/jetson_nano/catkin_ws/src/boat_control/src/boat_control/calibration_data.json
Currently, the Boat Control Package has only a single node called calibrate, however in the future you will have to implement further functionality like the sailing logic.
There is a directory for your python code in catkin_ws/src/boat_control/src/boat_control, where you can implement, for instance, your sailing node called sail.py.
In order to be able to start this node, you must create a "python launch file" in catkin_ws/src/boat_control/scripts, e.g. a file called sail.
Take a look at how we do it with the calibration node.
You could then launch the sailing node via rosrun boat_control sail.
The apm.py file abstracts the MAVROS communication with the APM board and contains utility functions to:
- get and set APM parameters
- set APM Mode (
MANUAL,AUTOMATIC) - read and set RC channels aka servos and sensors
It can be imported in python files inside the boat_control package.
See example usage in calibrate.py.
runnable by roslaunch camera <launchfile>
Runs the camera publisher node. Uses raw images for ROS darknet (YOLO) and the corrected frames for the depth map generation.
Runs the calibration python script.
Takes pictures from camera input if key s is pressed. After all images are taken, pressing x ends the image collection process.
Afterwards a stereoMap.xml file is generated and saved. All selected images are saved in /left/video_shot_{index} and /right/video_shot_{index}.
If multiple cameras are connected, change camport variable to desired port number.
Its recommended to use at least 10 sample images of the calibration chessboard.
- Explanations and tutorial of the calibration process YouTube: Camera Calibration
- Used source code from niconielsen32: ComputerVision
- Mathematical details OpenCV: Camera Calibration
Subscribes to video_frames ROS topic and corrects video feed by stereoMap.xml file. The video feed then is shown in its raw and corrected form split into its left and right parts.
Subscribes to camera/frame_corr_both ROS topic and generates a depth map. The following 3 parameters can be changed while the depth map is shown:
-
Number of disparities (from 1 to 17)
-
Block size (from 5 to 50)
-
Minimum number of disparities (from 5 to 25)
-
More information about these maps at learnopencv: depth perception
-
Mathematical details OpenCV: Depth Map
camera_pub.py reads an image from the camera, applies the correction via the stereoMap.xml file and publishes both frames (the corrected and original image) to the topics /camera/frame_corr_both and /camera/frame_both, respectively.
The camera publisher can be launched by
$ roslaunch camera camera_py.launchAdditionally, the optional arguments
sample_rate (default "10.0"): Frequency in Hz to publish images from the camera
verbose (default "true"): Verbose output
can be supplied for instance by
$ roslaunch camera camera_py.launch sample_rate:=0.5 verbose:=falseFor debugging and other purposes the published images may be displayed with
$ rosrun image_view image_view image:=/camera/frame_bothor
$ rosrun image_view image_view image:=/camera/frame_corr_both(Resizing the window may be necessary to see the entire image.)
When viewing the published image, note that the color channels may be swapped. This is intended behavior due to a quirk in YOLO.
The mediator is situated between the camera publisher and the YOLO node.
Based on user-specified launch arguments, it processes the image published by camera_pub.py to either the /camera/frame_corr_both or /camera/frame_both topic and publishes it to the /camera/rgb/image_raw topic at which point the YOLO node picks it up.
The mediator can be launched by
$ roslaunch camera yolo_mediator.launchOptional arguments can be supplied by:
$ roslaunch camera yolo_mediator.launch arg1:=value1 arg2:=value2To modify the behavior of the mediator, the following arguments are available to the user:
calib (default "true"): Use calibrated camera frame
frame_slice (default "right"): [left, right] camera for object detection
log_detection (default "false"): Print YOLO's detections with bounding box to terminal
scale_factor (default "1.0"): Factor to scale frame by
thresh (default "0.3"): Probability threshold for log_detection in range [0, 1]
verbose (default "true"): Verbose output
In order to obtain the above output:
$ roslaunch camera yolo_mediator.launch --ros-argsThe default values can be changed by modifying the launch file isp-2022/jetson_nano/catkin_ws/src/camera/launch/yolo_mediator.launch.
The scale_factor argument was implemented with the assumption that by varying the scaling of the image supplied to YOLO the speed/FPS of the detection is affected.
This, however, does not appear to be the case. The speed of the object detection seems to be independent of the input image's resolution since scaling the input image by 0.5 or even 0.25 leads to no change in speed. Hence, the default value of 1.0.
The YOLO node carries out the object detection and runs on the Darknet framework.
The input images to the node are supplied by publishing images to the /camera/rgb/image_raw topic.
The YOLO node can be launched by
$ roslaunch darknet_ros yolo_v3.launchfor detection with the YOLOv3 model or by
$ roslaunch darknet_ros yolo_v3_tiny.launchfor detection with the YOLOv3-tiny model.
To feed images to YOLO, the camera publisher and the mediator have to launched as well:
$ roslaunch camera camera_py.launch
$ roslaunch camera yolo_mediator.launchThe corresponding ROS graph may look like this:
(Image generated with the rqt_graph utility)
To ensure that the Jetson is running with the highest possible performance, set the Jetson to 10W (MAXN) performance mode:
$ sudo nvpmodel -m 0
$ sudo jetson_clocksWhen accessing the Jetson over SSH the -X flag can be used $ ssh -X nano@jetson-nano to see detected object's bounding boxes.
After cloning the repository for the very first time additional steps have to be taken to run the YOLO node.
The Darknet binaries have to be compiled in order to utilize the Jetson's GPU. To this end, invoke the following commands:
$ cd isp-2022/jetson_nano/catkin_ws/src/darknet_ros/darknet/
$ make(The required changes to the Makefile and further changes can be referred to by commit 1d35b94a).
Finally, build the ROS package in Release mode to increase performance:
$ cd isp-2022/jetson_nano/catkin_ws
$ ./release-build.sh
$ source devel/setup.bashFurther, the weights of the YOLOv3 and YOLOv3-tiny models have to download. For this, see the file how_to_download_weights.txt in the directory isp-2022/jetson_nano/catkin_ws/src/darknet_ros/darknet_ros/yolo_network_config/weights and place the weight files in the same directory.
YOLO Darknet Official Website YOLO Darknet + ROS GitHub Repository
Not in a working state!
There exists an additional ROS package isp-2022/jetson_nano/catkin_ws/src/yolov4_trt_ros as a submodule. It is currently not runnable due to numerous version/depencency incompatabilities.
The root cause of the issues appears to be the fact that we are running Ubuntu 20.04 while the CUDA and TensorRT packages are based on Ubuntu 18.
However, the advantes of running YOLO with TensoRT are significant. According to this blog post, YOLOv3 runs with ~4 FPS on a Jetson Nano. A substantial increase over the current 1.3-1.5 FPS when running YOLO with Darknet.
For further details see isp-2022/jetson_nano/catkin_ws/src/yolov4_trt_ros/ISSUES.md
- CMake needs to be 3.22 or newer. package onnxoptimizer needs cmake 3.22 or higher. Thus if needed a supported CMake Version is available in /YOEO/dep.
Installation:
./configure
make
sudo make install
cmake --version should now show version 3.24 (or the version which was installed)
- onxoptimizer 0.2.6 which is required in the default poetry.lock file is not available on ARCH64. Thus, version 0.3.0 was set in the changed .lock file.
- the same applies to onnxruntime-gpu (1.12.1 instead of 1.10.0). Of course a newer version can be tried out.
- If the user want to select a specific version himself, he also needs to add the sha265 hash of the setup archive.
- The old .lock file indicated by "_old" can be used to compare version differences.
Instead of generating bouding boxes in YOLO, the approach of segmentation is persued in YOEO. This can be helpful to separate certain areas. A practical example would be the segmentation of water and sky.
Abstract Fast and accurate visual perception utilizing a robot’s limited hardware resources is necessary for many mobile robot applications. We are presenting YOEO, a novel hybrid CNN which unifies previous object detection and semantic segmentation approaches using one shared encoder backbone to increase performance and accuracy. We show that it outperforms previous approaches on the TORSO-21 and Cityscapes datasets.
@inproceedings{vahlyoeo,
title={YOEO — You Only Encode Once: A CNN for Embedded Object Detection and Semantic Segmentation},
author={Vahl, Florian and Gutsche, Jan and Bestmann, Marc and Zhang, Jianwei},
year={2021},
organization={IEEE},
booktitle={2021 IEEE International Conference on Robotics and Biomimetics (ROBIO)}
}
You need a Jetson Nano running the following software:
- Ubuntu 20.04
- ROS Noetic Ninjemys
- MAVROS 0.32.0
- MAVLink 2020.2.2-1
Here we provide detailed step-by-step instructions on how we set up everything. This might be helpful for troubleshooting future problems.
Ubuntu 20.04 is required to run the latest ROS (ROS Noetic Ninjemys as of now). Unfortunately, the official Jetson Nano Developer Kit SD Card Image (4.6.1) uses Ubuntu 18.04 for the near future.
Hence, we use an unofficial image based on Xubuntu 20.04.
Extract the downloaded .tar.tbz2 and flash the image to the sdcard using your favorite tool, e.g. balenaEtcher.
Select keyboard layout, Connect to wifi-hotspot:
SSID: TP-Link_9C4A
Passwort: 94588637
and resize the app partition to the full size of the sdcard. Setup a user account. We chose:
user: nano
password: jetson
hostname: jetson-nano
Just plug in an ethernet cable providing Internet access
Since the custom sdcard image is a bit outdated, we should upgrade all packages first:
sudo apt update
sudo apt upgrade
There might be another process, i.e. a unattended-ugrade script, which blocks the update process.
You can look it up by the PID which is provided in the error message:
ps aux | grep <PID>
You must first wait for it to finish or reboot/kill it, e.g.
kill <PID>
sudo apt-get install openssh-server
sudo systemctl enable ssh
sudo systemctl start ssh
Get the Jetson's ip addr by running
ip address
Now you can connect via ssh from your machine:
ssh <username>@<ipaddress>
Using X11 forwarding
ssh <username>@<ipaddress> -X
Follow instructions at https://wiki.ros.org/noetic/Installation/Ubuntu unitl 2. Tutorials (select Desktop-Full install).
We install a custom MAVLINK/MAVROS version.
Geopgraphiclib must still be installed:
sudo apt-get install libgeographic-dev
sudo apt-get install geographiclib-tools
Run
wget https://raw.githubusercontent.com/mavlink/mavros/master/mavros/scripts/install_geographiclib_datasets.sh
sudo bash ./install_geographiclib_datasets.sh
In order to have the permissions to access the APM device at /dev/ttyACM0, we need to add our user to the dialout group and logout/login:
sudo adduser $USER dialout
First of all we have to establish a connection to GitLab in order to clone our repository. Inside the repo we added a root folder jetson_nano with a subfolder called catkin_ws.
This folder will be symlinked to the home folder, so that we can access the catkin workspace via
cd ~/catkin_ws
Installing CUDA is necessary for accelerating the object detection via YOLO. It is advisable to use CUDA 10.0 to avoid dependency and version incompatibility issues. To install CUDA 10.0:
$ sudo apt install \
cuda-command-line-tools-10-0 \
cuda-compiler-10-0 \
cuda-curand-dev-10-0 \
cuda-cublas-dev-10-0 \
libcudnn7-dev \
gcc-7 \
g++-7The older versions of gcc and g++ are required by the CUDA compiler nvcc.
Add CUDA to $PATH by adding
export PATH=/usr/local/cuda-10.0/bin${PATH:+:${PATH}}
to ~/.bashrc and $ source ~/.bashrc.
Installation
$ sudo apt install ros-noetic-vision-opencv
$ sudo apt install ros-noetic-cv-bridgeTo build OpenCV with CUDA support, look to the directory isp-2022/jetson_nano/build_opencv.
Further, this blog post and the resources it links to may be of help.
Note that compiling OpenCV from source on the Jetson Nano takes several hours. Hence, it is advisable to start the build job in a tmux session from which detaching without stopping the job is possible.
See this link for various camera configuration options that can be set by via OpenCV
cap = cv2.VideoCapture(cv2.CAP_ANY)
cap.set(<index>, <value>)git clone https://github.com/bit-bots/YOEO
cd YOEO/
pip3 install poetry --user
PATH=$PATH:/home/parallels/.local/bin
poetry install
Initially, we had issues to establish the MAVLink connection with the APM board. The main issue is that the APM board uses an old firmware that is not compatible with recent MAVLink implementations.
Inspired by this GitHub issue we tried the following mavros/mavlink versions:
mavros: release 0.32.0
mavlink: release 2020.2.2-1
Following the source installation instructions we came up with this .rosinstall file:
- git:
local-name: mavlink
uri: https://github.com/mavlink/mavlink-gbp-release.git
version: release/melodic/mavlink/2020.2.2-1
- git:
local-name: mavros
uri: https://github.com/mavlink/mavros.git
version: 0.32.0
Note: The installation instructions are for an older ROS version.
We have to replace catkin build with catkin_make_isolated.
However, running catkin_make_isolated showed some errors that had to be fixed first.
future for python2 was missing, i.e. python2 -m pip install future had to be run
To compile, libboost1.67 was required for the call to Socket::get_io_service() to work, but ROS noetic depends on version 1.71.
Thus, a downgrade was not an option.
As a workaround, we replaced the calls to the deprecated function Socket::get_io_service() in the source file catkin_ws_mavros/src/mavros/libmavconn/src/tcp.cpp according to this stackoverflow answer.
Since we installed mavros through the debian package manager previously, we have to remove these packages to avoid conflicts.
sudo apt-get purge ros-noetix-mavros ros-noetic-mavros-extras ros-noetic-mavlink
Now we can source the isolated development environment
nano@jetson-nano:~/catkin_ws_mavros$ source devel_isolated/setup.bash
and connect to the APM board with
roslaunch mavros apm.launch fcu_url:=serial:///dev/ttyACM0:115200 fcu_protocol:=v1.0
You should see Ready to drive at some point, indicating that the connection was successfull.
Now you can set parameters, e.g.
nano@jetson-nano:~/gitlab_repo/jetson_nano/catkin_ws_mavros/devel_isolated/mavros/lib/mavros$ rosrun mavros mavparam set RUDDER_MIN 100
100
and get parameters, e.g.
nano@jetson-nano:~/gitlab_repo/jetson_nano/catkin_ws_mavros/devel_isolated/mavros/lib/mavros$ rosrun mavros mavparam get RUDDER_MIN
100
... logging to /home/nano/.ros/log/3e774a90-17e6-11ed-bbff-e5481d40a7d3 /roslaunch-jetson-nano-7926.log
Checking log directory for disk usage. This may take a while.
Press Ctrl-C to interrupt
Done checking log file disk usage. Usage is <1GB.
started roslaunch server http://jetson-nano:37083/
SUMMARY
========
CLEAR PARAMETERS
* /mavros/
PARAMETERS
* /mavros/cmd/use_comp_id_system_control: False
* /mavros/conn/heartbeat_mav_type: ONBOARD_CONTROLLER
* /mavros/conn/heartbeat_rate: 1.0
* /mavros/conn/system_time_rate: 1.0
* /mavros/conn/timeout: 10.0
* /mavros/conn/timesync_rate: 10.0
* /mavros/distance_sensor/rangefinder_pub/field_of_view: 0.0
* /mavros/distance_sensor/rangefinder_pub/frame_id: lidar
* /mavros/distance_sensor/rangefinder_pub/id: 0
* /mavros/distance_sensor/rangefinder_pub/send_tf: False
* /mavros/distance_sensor/rangefinder_pub/sensor_position/x: 0.0
* /mavros/distance_sensor/rangefinder_pub/sensor_position/y: 0.0
* /mavros/distance_sensor/rangefinder_pub/sensor_position/z: -0.1
* /mavros/distance_sensor/rangefinder_sub/id: 1
* /mavros/distance_sensor/rangefinder_sub/orientation: PITCH_270
* /mavros/distance_sensor/rangefinder_sub/subscriber: True
* /mavros/fake_gps/eph: 2.0
* /mavros/fake_gps/epv: 2.0
* /mavros/fake_gps/fix_type: 3
* /mavros/fake_gps/geo_origin/alt: 408.0
* /mavros/fake_gps/geo_origin/lat: 47.3667
* /mavros/fake_gps/geo_origin/lon: 8.55
* /mavros/fake_gps/gps_rate: 5.0
* /mavros/fake_gps/mocap_transform: True
* /mavros/fake_gps/satellites_visible: 5
* /mavros/fake_gps/tf/child_frame_id: fix
* /mavros/fake_gps/tf/frame_id: map
* /mavros/fake_gps/tf/listen: False
* /mavros/fake_gps/tf/rate_limit: 10.0
* /mavros/fake_gps/tf/send: False
* /mavros/fake_gps/use_mocap: True
* /mavros/fake_gps/use_vision: False
* /mavros/fcu_protocol: v1.0
* /mavros/fcu_url: serial:///dev/tty...
* /mavros/gcs_url:
* /mavros/global_position/child_frame_id: base_link
* /mavros/global_position/frame_id: map
* /mavros/global_position/gps_uere: 1.0
* /mavros/global_position/rot_covariance: 99999.0
* /mavros/global_position/tf/child_frame_id: base_link
* /mavros/global_position/tf/frame_id: map
* /mavros/global_position/tf/global_frame_id: earth
* /mavros/global_position/tf/send: False
* /mavros/global_position/use_relative_alt: True
* /mavros/image/frame_id: px4flow
* /mavros/imu/angular_velocity_stdev: 0.0003490659 // 0...
* /mavros/imu/frame_id: base_link
* /mavros/imu/linear_acceleration_stdev: 0.0003
* /mavros/imu/magnetic_stdev: 0.0
* /mavros/imu/orientation_stdev: 1.0
* /mavros/landing_target/camera/fov_x: 2.0071286398
* /mavros/landing_target/camera/fov_y: 2.0071286398
* /mavros/landing_target/image/height: 480
* /mavros/landing_target/image/width: 640
* /mavros/landing_target/land_target_type: VISION_FIDUCIAL
* /mavros/landing_target/listen_lt: False
* /mavros/landing_target/mav_frame: LOCAL_NED
* /mavros/landing_target/target_size/x: 0.3
* /mavros/landing_target/target_size/y: 0.3
* /mavros/landing_target/tf/child_frame_id: camera_center
* /mavros/landing_target/tf/frame_id: landing_target
* /mavros/landing_target/tf/listen: False
* /mavros/landing_target/tf/rate_limit: 10.0
* /mavros/landing_target/tf/send: True
* /mavros/local_position/frame_id: map
* /mavros/local_position/tf/child_frame_id: base_link
* /mavros/local_position/tf/frame_id: map
* /mavros/local_position/tf/send: False
* /mavros/local_position/tf/send_fcu: False
* /mavros/mission/pull_after_gcs: True
* /mavros/mocap/use_pose: True
* /mavros/mocap/use_tf: False
* /mavros/odometry/estimator_type: 3
* /mavros/odometry/frame_tf/desired_frame: ned
* /mavros/plugin_blacklist: [actuator_contro...
* /mavros/plugin_whitelist: []
* /mavros/px4flow/frame_id: px4flow
* /mavros/px4flow/ranger_fov: 0.118682
* /mavros/px4flow/ranger_max_range: 5.0
* /mavros/px4flow/ranger_min_range: 0.3
* /mavros/safety_area/p1/x: 1.0
* /mavros/safety_area/p1/y: 1.0
* /mavros/safety_area/p1/z: 1.0
* /mavros/safety_area/p2/x: -1.0
* /mavros/safety_area/p2/y: -1.0
* /mavros/safety_area/p2/z: -1.0
* /mavros/setpoint_accel/send_force: False
* /mavros/setpoint_attitude/reverse_thrust: False
* /mavros/setpoint_attitude/tf/child_frame_id: target_attitude
* /mavros/setpoint_attitude/tf/frame_id: map
* /mavros/setpoint_attitude/tf/listen: False
* /mavros/setpoint_attitude/tf/rate_limit: 50.0
* /mavros/setpoint_attitude/use_quaternion: False
* /mavros/setpoint_position/mav_frame: LOCAL_NED
* /mavros/setpoint_position/tf/child_frame_id: target_position
* /mavros/setpoint_position/tf/frame_id: map
* /mavros/setpoint_position/tf/listen: False
* /mavros/setpoint_position/tf/rate_limit: 50.0
* /mavros/setpoint_raw/thrust_scaling: 1.0
* /mavros/setpoint_velocity/mav_frame: LOCAL_NED
* /mavros/startup_px4_usb_quirk: False
* /mavros/sys/disable_diag: False
* /mavros/sys/min_voltage: 10.0
* /mavros/target_component_id: 1
* /mavros/target_system_id: 1
* /mavros/tdr_radio/low_rssi: 40
* /mavros/time/time_ref_source: fcu
* /mavros/time/timesync_avg_alpha: 0.6
* /mavros/time/timesync_mode: MAVLINK
* /mavros/vibration/frame_id: base_link
* /mavros/vision_pose/tf/child_frame_id: vision_estimate
* /mavros/vision_pose/tf/frame_id: map
* /mavros/vision_pose/tf/listen: False
* /mavros/vision_pose/tf/rate_limit: 10.0
* /mavros/vision_speed/listen_twist: True
* /mavros/vision_speed/twist_cov: True
* /mavros/wheel_odometry/child_frame_id: base_link
* /mavros/wheel_odometry/count: 2
* /mavros/wheel_odometry/frame_id: map
* /mavros/wheel_odometry/send_raw: True
* /mavros/wheel_odometry/send_twist: False
* /mavros/wheel_odometry/tf/child_frame_id: base_link
* /mavros/wheel_odometry/tf/frame_id: map
* /mavros/wheel_odometry/tf/send: True
* /mavros/wheel_odometry/use_rpm: False
* /mavros/wheel_odometry/vel_error: 0.1
* /mavros/wheel_odometry/wheel0/radius: 0.05
* /mavros/wheel_odometry/wheel0/x: 0.0
* /mavros/wheel_odometry/wheel0/y: -0.15
* /mavros/wheel_odometry/wheel1/radius: 0.05
* /mavros/wheel_odometry/wheel1/x: 0.0
* /mavros/wheel_odometry/wheel1/y: 0.15
* /rosdistro: noetic
* /rosversion: 1.15.14
NODES
/
mavros (mavros/mavros_node)
auto-starting new master
process[master]: started with pid [7934]
ROS_MASTER_URI=http://localhost:11311
setting /run_id to 3e774a90-17e6-11ed-bbff-e5481d40a7d3
process[rosout-1]: started with pid [7946]
started core service [/rosout]
process[mavros-2]: started with pid [7964]
[ INFO] [1660051319.821902237]: FCU URL: serial:///dev/ttyACM0:115200
[ INFO] [1660051319.830922456]: serial0: device: /dev/ttyACM0 @ 115200 bps
[ INFO] [1660051319.831991591]: GCS bridge disabled
[ INFO] [1660051319.868104136]: Plugin 3dr_radio loaded
[ INFO] [1660051319.874696394]: Plugin 3dr_radio initialized
[ INFO] [1660051319.874804573]: Plugin actuator_control blacklisted
[ INFO] [1660051319.885851393]: Plugin adsb loaded
[ INFO] [1660051319.898116881]: Plugin adsb initialized
[ INFO] [1660051319.898216675]: Plugin altitude blacklisted
[ INFO] [1660051319.898606734]: Plugin cam_imu_sync loaded
[ INFO] [1660051319.901147615]: Plugin cam_imu_sync initialized
[ INFO] [1660051319.901605748]: Plugin command loaded
[ INFO] [1660051319.923383236]: Plugin command initialized
[ INFO] [1660051319.923831473]: Plugin companion_process_status loaded
[ INFO] [1660051319.933357900]: Plugin companion_process_status initialized
[ INFO] [1660051319.933461027]: Plugin debug_value blacklisted
[ INFO] [1660051319.933927651]: Plugin distance_sensor loaded
[ INFO] [1660051319.966066633]: Plugin distance_sensor initialized
[ INFO] [1660051319.966622789]: Plugin fake_gps loaded
[ INFO] [1660051320.016780023]: Plugin fake_gps initialized
[ INFO] [1660051320.016893306]: Plugin ftp blacklisted
[ INFO] [1660051320.017292376]: Plugin global_position loaded
[ INFO] [1660051320.074467240]: Plugin global_position initialized
[ INFO] [1660051320.075000374]: Plugin gps_rtk loaded
[ INFO] [1660051320.084409664]: Plugin gps_rtk initialized
[ INFO] [1660051320.084517687]: Plugin hil blacklisted
[ INFO] [1660051320.084972955]: Plugin home_position loaded
[ INFO] [1660051320.098885870]: Plugin home_position initialized
[ INFO] [1660051320.099324576]: Plugin imu loaded
[ INFO] [1660051320.127881825]: Plugin imu initialized
[ INFO] [1660051320.128385168]: Plugin landing_target loaded
[ INFO] [1660051320.179948261]: Plugin landing_target initialized
[ INFO] [1660051320.180383737]: Plugin local_position loaded
[ INFO] [1660051320.204632521]: Plugin local_position initialized
[ INFO] [1660051320.205189094]: Plugin log_transfer loaded
[ INFO] [1660051320.217138795]: Plugin log_transfer initialized
[ INFO] [1660051320.217577709]: Plugin manual_control loaded
[ INFO] [1660051320.229106413]: Plugin manual_control initialized
[ INFO] [1660051320.229595797]: Plugin mocap_pose_estimate loaded
[ INFO] [1660051320.243718663]: Plugin mocap_pose_estimate initialized
[ INFO] [1660051320.244145650]: Plugin mount_control loaded
[ INFO] [1660051320.253465771]: Plugin mount_control initialized
[ INFO] [1660051320.253946041]: Plugin obstacle_distance loaded
[ INFO] [1660051320.263265120]: Plugin obstacle_distance initialized
[ INFO] [1660051320.263741587]: Plugin odom loaded
[ INFO] [1660051320.284322178]: Plugin odom initialized
[ INFO] [1660051320.284946616]: Plugin param loaded
[ INFO] [1660051320.297347211]: Plugin param initialized
[ INFO] [1660051320.297469766]: Plugin px4flow blacklisted
[ INFO] [1660051320.298003734]: Plugin rangefinder loaded
[ INFO] [1660051320.300770243]: Plugin rangefinder initialized
[ INFO] [1660051320.301282440]: Plugin rc_io loaded
[ INFO] [1660051320.315254106]: Plugin rc_io initialized
[ INFO] [1660051320.315365671]: Plugin safety_area blacklisted
[ INFO] [1660051320.315761043]: Plugin setpoint_accel loaded
[ INFO] [1660051320.327766578]: Plugin setpoint_accel initialized
[ INFO] [1660051320.328467320]: Plugin setpoint_attitude loaded
[ INFO] [1660051320.366057081]: Plugin setpoint_attitude initialized
[ INFO] [1660051320.366531464]: Plugin setpoint_position loaded
[ INFO] [1660051320.415987279]: Plugin setpoint_position initialized
[ INFO] [1660051320.416424943]: Plugin setpoint_raw loaded
[ INFO] [1660051320.449987962]: Plugin setpoint_raw initialized
[ INFO] [1660051320.450517034]: Plugin setpoint_velocity loaded
[ INFO] [1660051320.472557756]: Plugin setpoint_velocity initialized
[ INFO] [1660051320.473350323]: Plugin sys_status loaded
[ INFO] [1660051320.515820591]: Plugin sys_status initialized
[ INFO] [1660051320.516317579]: Plugin sys_time loaded
[ INFO] [1660051320.537405523]: TM: Timesync mode: MAVLINK
[ INFO] [1660051320.541956493]: Plugin sys_time initialized
[ INFO] [1660051320.542435824]: Plugin trajectory loaded
[ INFO] [1660051320.562944747]: Plugin trajectory initialized
[ INFO] [1660051320.563420068]: Plugin vfr_hud loaded
[ INFO] [1660051320.565982355]: Plugin vfr_hud initialized
[ INFO] [1660051320.566114076]: Plugin vibration blacklisted
[ INFO] [1660051320.566535855]: Plugin vision_pose_estimate loaded
[ INFO] [1660051320.595289149]: Plugin vision_pose_estimate initialized
[ INFO] [1660051320.595418996]: Plugin vision_speed_estimate blacklisted
[ INFO] [1660051320.595853691]: Plugin waypoint loaded
[ INFO] [1660051320.613431778]: Plugin waypoint initialized
[ INFO] [1660051320.613561780]: Plugin wheel_odometry blacklisted
[ INFO] [1660051320.613951579]: Plugin wind_estimation loaded
[ INFO] [1660051320.617368205]: Plugin wind_estimation initialized
[ INFO] [1660051320.617639564]: Built-in SIMD instructions: ARM NEON
[ INFO] [1660051320.617821078]: Built-in MAVLink package version:
[ INFO] [1660051320.617928059]: Known MAVLink dialects: common ardupilotmega ASLUAV autoquad icarous matrixpilot paparazzi slugs standard uAvionix ualberta
[ INFO] [1660051320.618007332]: MAVROS started. MY ID 1.240, TARGET ID 1.1
[ERROR] [1660051321.543604567]: FCU: Calibrating barometer
[ INFO] [1660051321.545143137]: CON: Got HEARTBEAT, connected. FCU: ArduPilot
[ERROR] [1660051322.595141531]: FCU: command received:
[ INFO] [1660051322.600939295]: VER: 1.1: Capabilities 0x0000000000000000
[ INFO] [1660051322.603522780]: VER: 1.1: Flight software: 00000080 (e43baa2c)
[ INFO] [1660051322.605092132]: VER: 1.1: Middleware software: 00000000 ( )
[ INFO] [1660051322.606112880]: VER: 1.1: OS software: 00000000 ( )
[ INFO] [1660051322.606480439]: VER: 1.1: Board hardware: 00000000
[ INFO] [1660051322.606892739]: VER: 1.1: VID/PID: 0000:0000
[ INFO] [1660051322.607725775]: VER: 1.1: UID: 0000000000000000
[ WARN] [1660051322.608780586]: CMD: Unexpected command 520, result 0
[ERROR] [1660051322.912485232]: FCU: Warming up ADC...
[ERROR] [1660051323.415141225]: FCU: Beginning INS calibration; do not move vehicle
[ERROR] [1660051323.542423951]: FCU: <startup_ground> GROUND START
[ERROR] [1660051325.556971315]: FCU: Initialising APM...
[ERROR] [1660051326.659895304]: FCU:
Ready to drive.
Laptop
User name: projekt
Password: mteclabor2022
Laptop VM
User name: developer
Password: password
Jetson Ubuntu 20.04
User name: nano
Password: jetson
Mac-Address: 28:D0:EA:88:52:DE
WiFi
SSID: TP-Link_9C4A
Password: 94588637
WiFi Admin Panel
IP: 192.168.0.1
Password: mtecsail2022