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PX4 avoidance ROS node for obstacle detection and avoidance.
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baumanta and jkflying Launchfile required (#400)
* make all nodes required

* change subordinate launchfiles

* make struct core required

* throttle depth image of structure core and remap for logging

If you get an error on argument required does not exist or similar when launching an autogenerated launchfile, please cherry-pick the mentoned commit in this PR onto your realsense wrapper
Latest commit 236bc0e Jun 24, 2019

Obstacle Detection and Avoidance

PX4 computer vision algorithms packaged as ROS nodes for depth sensor fusion and obstacle avoidance. This repository contains two different implementations:

  • local_planner is a local VFH+* based planner that plans (including some history) in a vector field histogram
  • global_planner is a global, graph based planner that plans in a traditional octomap occupancy grid
  • safe_landing_planner is a local planner to find safe area to land

The three algorithms are standalone and they are not meant to be used together.

The local_planner requires less computational power but it doesn't compute optimal paths towards the goal since it doesn't store information about the already explored environment. An in-depth discussion on how it works can be found in this thesis. On the other hand, the global_planner is computationally more expensive since it builds a map of the environment. For the map to be good enough for navigation, accurate global position and heading are required. An in-depth discussion on how it works can be found in this thesis. The safe_landing_planner classifies the terrain underneath the vehicle based on the mean and standard deviation of the z coordinate of pointcloud points. The pointcloud from a downwards facing sensor is binned into a 2D grid based on the xy point coordinates. For each bin, the mean and standard deviation of z coordinate of the points are calculated and they are used to locate flat areas where it is safe to land.

Note The development team is right now focused on the local_planner.

The documentation contains information about how to setup and run the two planner systems on the Gazebo simulator and on a companion computer running Ubuntu 16.04, for both avoidance and collision prevention use cases.

Note PX4-side setup is covered in the PX4 User Guide:

PX4 Avoidance video

Table of Contents

Getting Started


Installation for Ubuntu 16.04 and ROS Kinetic

This is a step-by-step guide to install and build all the prerequisites for running this module on Ubuntu 16.04. You might want to skip some of them if your system is already partially installed.

Note that in the following instructions, we assume your catkin workspace (in which we will build the avoidance module) is in ~/catkin_ws, and the PX4 Firmware directory is ~/Firmware. Feel free to adapt this to your situation.

  1. Add ROS to sources.list.

    echo "deb $(lsb_release -sc) main" > /etc/apt/sources.list.d/ros-latest.list
    sudo apt-key adv --keyserver 'hkp://' --recv-key C1CF6E31E6BADE8868B172B4F42ED6FBAB17C654
    sudo apt update
  2. Install gazebo with ROS.

    sudo apt install ros-kinetic-desktop-full
    # Source ROS
    source /opt/ros/kinetic/setup.bash

Full installation of ROS Kinetic comes with Gazebo 7.

If you are using different version of Gazebo,

please make sure install ros-gazebo related packages

For Gazebo 8,

sudo apt install ros-kinetic-gazebo8-*

For Gazebo 9,

sudo apt install ros-kinetic-gazebo9-*
  1. Initialize rosdep.

    rosdep init
    rosdep update
  2. Install catkin and create your catkin workspace directory.

    sudo apt install python-catkin-tools
    mkdir -p ~/catkin_ws/src
  3. Install mavros version 0.29.0 or above. Instructions to install it from sources can be found here: If you want to install using apt, be sure to check that the version is 0.29.0 or greater.

    sudo apt install ros-kinetic-mavros ros-kinetic-mavros-extras
  4. Install the geographiclib dataset

    chmod +x
    sudo ./
  5. Install avoidance module dependencies (pointcloud library and octomap).

    sudo apt install libpcl1 ros-kinetic-octomap-* ros-kinetic-yaml-*
  6. Clone this repository in your catkin workspace in order to build the avoidance node.

    cd ~/catkin_ws/src
    git clone
  7. Actually build the avoidance node.

    catkin build -w ~/catkin_ws

    Note that you can build the node in release mode this way:

    catkin build -w ~/catkin_ws --cmake-args -DCMAKE_BUILD_TYPE=Release
  8. Source the catkin setup.bash from your catkin workspace.

    source ~/catkin_ws/devel/setup.bash

Run the Avoidance Gazebo Simulation

In the following section we guide you trough installing and running a Gazebo simulation of both local and global planner.

Build and Run the Simulator

  1. Clone the PX4 Firmware and all its submodules (it may take some time).

    cd ~
    git clone
    cd ~/Firmware
    git submodule update --init --recursive
  2. Install PX4 dependencies. A complete list is available on the PX4 Dev Guide.

  3. We will now build the Firmware once in order to generate SDF model files for Gazebo. This step will actually run a simulation that you can directly quit.

    # This is necessary to prevent some Qt-related errors (feel free to try to omit it)
    export QT_X11_NO_MITSHM=1
    # Build and run simulation
    make px4_sitl_default gazebo
    # Setup some more Gazebo-related environment variables
    . ~/Firmware/Tools/setup_gazebo.bash ~/Firmware ~/Firmware/build/px4_sitl_default
  4. Add the Firmware directory to ROS_PACKAGE_PATH so that ROS can start PX4.

    export ROS_PACKAGE_PATH=${ROS_PACKAGE_PATH}:~/Firmware
  5. Finally, set the GAZEBO_MODEL_PATH in your bashrc:

echo export GAZEBO_MODEL_PATH=${GAZEBO_MODEL_PATH}:~/catkin_ws/src/avoidance/avoidance/sim/models:~/catkin_ws/src/avoidance/avoidance/sim/worlds >> ~/.bashrc

You should now be ready to run the simulation using local or global planner.

Global Planner

This section shows how to start the global_planner and use it for avoidance in offboard mode.

roslaunch global_planner global_planner_stereo.launch

You should now see the drone unarmed on the ground in a forest environment as pictured below.

Screenshot showing gazebo and rviz

To start flying, put the drone in OFFBOARD mode and arm it. The avoidance node will then take control of it.

# In another terminal
rosrun mavros mavsys mode -c OFFBOARD
rosrun mavros mavsafety arm

Initially the drone should just hover at 3.5m altitude.

From the command line, you can also make Gazebo follow the drone, if you want.

gz camera --camera-name=gzclient_camera --follow=iris

One can plan a new path by setting a new goal with the 2D Nav Goal button in rviz. The planned path should show up in rviz and the drone should follow the path, updating it when obstacles are detected. It is also possible to set a goal without using the obstacle avoidance (i.e. the drone will go straight to this goal and potentially collide with obstacles). To do so, set the position with the 2D Pose Estimate button in rviz.

Local Planner

This section shows how to start the local_planner and use it for avoidance in mission or offboard mode.

The planner is based on the 3DVFH+ algorithm. To run the algorithm it is possible to

  • simulate a forward looking stereo camera running OpenCV's block matching algorithm (SGBM by default)

    # if stereo-image-proc not yet installed
    sudo apt install ros-kinetic-stereo-image-proc
    roslaunch local_planner local_planner_stereo.launch

    The disparity map from stereo-image-proc is published as a stereo_msgs/DisparityImage message, which is not supported by rviz or rqt. To visualize the message, either run:

    # if image_view is not yet installed
    sudo apt install ros-kinetic-image-view
    rosrun image_view stereo_view stereo:=/stereo image:=image_rect_color

    or publish the DisparityImage as a simple sensor_msgs/Image

    rosrun topic_tools transform /stereo/disparity /stereo/disparity_image sensor_msgs/Image 'm.image'

    Now the disparity map can be visualized by rviz or rqt under the topic /stereo/disparity_image.

  • simulate a forward looking kinect depth sensor:

    roslaunch local_planner local_planner_depth-camera.launch
  • simulate three kinect depth sensors:

    roslaunch local_planner local_planner_sitl_3cam.launch
  • simulate one Intel Realsense camera:

    git clone
    catkin build realsense_gazebo_plugin #(for ROS kinetic, the kinetic-devel branch must be used)
    export GAZEBO_MODEL_PATH=${GAZEBO_MODEL_PATH}:~/catkin_ws/src/realsense_gazebo_plugin/models
    roslaunch local_planner local_planner_realsense.launch

You will see the Iris drone unarmed in the Gazebo world. To start flying, there are two options: OFFBOARD or MISSION mode. For OFFBOARD, run:

# In another terminal
rosrun mavros mavsys mode -c OFFBOARD
rosrun mavros mavsafety arm

The drone will first change its altitude to reach the goal height. It is possible to modify the goal altitude with rqt_reconfigure GUI. Screenshot rqt_reconfigure goal height Then the drone will start moving towards the goal. The default x, y goal position can be changed in Rviz by clicking on the 2D Nav Goal button and then choosing the new goal x and y position by clicking on the visualized gray space. If the goal has been set correctly, a yellow sphere will appear where you have clicked in the grey world. Screenshot rviz goal selection

For MISSIONS, open QGroundControl and plan a mission as described here. Set the parameter COM_OBS_AVOID true. Start the mission and the vehicle will fly the mission waypoints dynamically recomputing the path such that it is collision free.

Safe Landing Planner

This section shows how to start the safe_landing_planner and use it to land safely in mission or auto land mode. To run the node:

roslaunch safe_landing_planner safe_landing_planner.launch

You will see an unarmed vehicle on the ground. Open QGroundControl, either plan a mission with the last item of type Land or fly around the world in Position Control, click the Land button on the left side where you wish to land. At the land position, the vehicle will start to descend towards the ground until it is at loiter_height from the ground/obstacle. Then it will start loitering to evaluate the ground underneeth. If the ground is flat, the vehicle will continue landing. Otherwise it will evaluate the close by terrain in a squared spiral pattern until it finds a good enough ground to land on.

Run on Hardware



Both planners require a 3D point cloud of type sensor_msgs::PointCloud2. Any camera that can provide such data is compatible.

The officially supported camera is Intel Realsense D435. We recommend using Firmware version The instructions on how to update the Firmware of the camera can be found here

Tip: Be careful when attaching the camera with a USB3 cable. USB3 might might interfere with GPS and other signals. If possible, always use USB2 cables.

Other tested camera models are: Intel Realsense D415 and R200, Occipital Structure Core.

Generating Point-clouds from Depth-maps

In case the point-cloud stream already exists, this step can be skipped.

Assuming there already exists a stream of depth-maps on the ROS-topic <depthmap_topic>, we need to generate a corresponding stream of depth-maps. Start by following the instructions from PX4/disparity_to_point_cloud. Now run the point-cloud generation with the parameters for the camera intrinsics:

rosrun disparity_to_point_cloud disparity_to_point_cloud_node \
    fx_:=fx fy_:=fy cx_:=cx cy_:=cy base_line_:=base_line disparity:=<depthmap_topic>

A stream of point-clouds should now be published to /point_cloud.

PX4 Autopilot

Parameters to set through QGC:

  • COM_OBS_AVOID to Enabled
  • MAV_1_CONFIG, MAV_1_MODE, SER_TEL2_BAUD to enable MAVLink on a serial port. For more information: PX4 Dev Guide

Companion Computer

  • OS: Ubuntu 16.04 OS or a docker container running Ubuntu 16.04 must be setup (e.g. if using on a Yocto based system).
  • ROS Kinetic: see Installation
  • Other Required Components for Intel Realsense:
  • Other Required Components for Occipital Structure Core:
    • Download the Structure SDK. The version tested with this package is 0.7.1. Create the build directory and build the SDK
    mkdir build
    cd build
    cmake ..
    • Clone the ROS wrapper in the catkin_ws
    • Copy the shared object Libraries/Structure/Linux/x86_64/ from the SDK into /usr/local/lib/
    • Copy the headers from Libraries/Structure/Headers/ in the SDK to the ROS wrapper include directory ~/catkin_ws/src/struct_core_ros/include

Tested models:

  • local planner: Intel NUC, Jetson TX2, Intel Atom x7-Z8750 (built-in on Intel Aero RTF drone)
  • global planner: Odroid

Global Planner

The global planner has been so far tested on a Odroid companion computer by the development team.

Local Planner

Once the catkin workspace has been built, to run the planner with a Realsense D435 or Occipital Structure Core camera you can generate the launch file using the script

  1. export CAMERA_CONFIGS="camera_namespace, camera_type, serial_n, tf_x, tf_y, tf_z, tf_yaw, tf_pitch, tf_roll" where camera_type is either realsense or struct_core_ros, tf_* represents the displacement between the camera and the flight controller. If more than one camera is present, list the different camera configuration separated by a semicolon. Within each camera configuration the parameters are separated by commas.
  2. export DEPTH_CAMERA_FRAME_RATE=frame_rate. If this variable isn't set, the default frame rate will be taken.
  3. export VEHICLE_CONFIG=/path/to/params.yaml where the yaml file contains the value of some parameters different from the defaults set in the cfg file. If this variable isn't set, the default parameters values will be used.

Changing the serial number and DEPTH_CAMERA_FRAME_RATE don't have any effect on the Structure Core.

For example:

export CAMERA_CONFIGS="camera_main,realsense,819612070807,0.3,0.32,-0.11,0,0,0"
export VEHICLE_CONFIG=~/catkin_ws/src/avoidance/local_planner/cfg/params_vehicle_1.yaml
roslaunch local_planner avoidance.launch fcu_url:=/dev/ttyACM0:57600

where fcu_url representing the port connecting the companion computer to the flight controller. The planner is running correctly if the rate of the processed point cloud is around 10-20 Hz. To check the rate run:

rostopic hz /local_pointcloud

If you would like to read debug statements on the console, please change custom_rosconsole.conf to


Safe Landing Planner

Once the catkin workspace has been built, to run the planner with a Realsense D435 and Occipital Structure Core, you can generate the launch file using the script The script works the same as described in the section above for the Local Planner. For example:

export CAMERA_CONFIGS="camera_main,struct_core,819612070807,0.3,0.32,-0.11,0,0,0"
export VEHICLE_CONFIG_SLP=~/catkin_ws/src/avoidance/safe_landing_planner/cfg/slpn_structure_core.yaml
export VEHICLE_CONFIG_WPG=~/catkin_ws/src/avoidance/safe_landing_planner/cfg/wpgn_structure_core.yaml
roslaunch safe_landing_planner safe_landing_planner_launch.launch

In the cfg/ folder there are camera specific configurations for the algorithm nodes. These parameters can be loaded by specifying the file in the VEHICLE_CONFIG_SLP and VEHICLE_CONFIG_WPG system variable for the safe_landing_planner_node and for the waypoint_generator_node respectively.

The size of the squared shape patch of terrain below the vehicle that is evaluated by the algorithm can be changed to suit different vehicle sizes with the WaypointGeneratorNode parameter smoothing_land_cell. The algorithm behavior will also be affected by the height at which the decision to land or not is taken (loiter_height parameter in WaypointGeneratorNode) and by the size of neighborhood filter smoothing (smoothing_size in LandingSiteDetectionNode).

For different cameras you might also need to tune the thresholds on the number of points in each bin, standard deviation and mean.


I see the drone position in rviz (shown as a red arrow), but the world around is empty

Check that some camera topics (including /camera/depth/points) are published with the following command:

rostopic list | grep camera

If /camera/depth/points is the only one listed, it may be a sign that gazebo is not actually publishing data from the simulated depth camera. Verify this claim by running:

rostopic echo /camera/depth/points

When everything runs correctly, the previous command should show a lot of unreadable data in the terminal. If you don't receive any message, it probably means that gazebo is not publishing the camera data.

Check that the clock is being published by Gazebo:

rostopic echo /clock

If it is not, you have a problem with Gazebo (Did it finish loading the world? Do you see the buildings and the drone in the Gazebo UI?). However, if it is publishing the clock, then it might be a problem with the depth camera plugin. Make sure the package ros-kinetic-gazebo-ros-pkgs is installed. If not, install it and rebuild the Firmware (with $ make px4_sitl_default gazebo as explained above).

I see the drone and world in rviz, but the drone does not move when I set a new "2D Nav Goal"

Is the drone in OFFBOARD mode? Is it armed and flying?

# Set the drone to OFFBOARD mode
rosrun mavros mavsys mode -c OFFBOARD
# Arm
rosrun mavros mavsafety arm

I see the drone and world in rviz, but the drone does not follow the path properly

Some tuning may be required in the file "<Firmware_dir>/posix-configs/SITL/init/rcS_gazebo_iris".

I see the drone and world in rviz, I am in OFFBOARD mode, but the planner is still not working

Some parameters that can be tuned in rqt reconfigure.


Message Flows

More information about the communication between avoidance system and the Autopilot can be found in the PX4 User Guide

PX4 and local planner

This is the complete message flow from PX4 Firmware to the local planner.

PX4 topic MAVLink MAVROS Plugin ROS Msgs. ROS Topic
vehicle_local_position LOCAL_POSITION_NED local_position geometry_msgs::PoseStamped mavros/local_position/pose
vehicle_local_position LOCAL_POSITION_NED local_position geometry_msgs::TwistStamped mavros/local_position/velocity
vehicle_local_position ALTITUDE altitude mavros_msgs::Altitude mavros/altitude
home_position ALTITUDE altitude mavros_msgs::Altitude mavros/altitude
vehicle_air_data ALTITUDE altitude mavros_msgs::Altitude mavros/altitude
vehicle_status HEARTBEAT sys_status mavros_msgs::State mavros/state
vehicle_trajectory_waypoint_desired TRAJECTORY_REPRESENTATION_WAYPOINT trajectory mavros_msgs::Trajectory mavros/trajectory/desired

This is the complete message flow to PX4 Firmware from the local planner.

ROS topic ROS Msgs. MAVROS Plugin MAVLink PX4 Topic
/mavros/setpoint_position/local (offboard) geometry_msgs::PoseStamped setpoint_position SET_POSITION_LOCAL_POSITION_NED position_setpoint_triplet
/mavros/setpoint_velocity/cmd_vel_unstamped (offboard) geometry_msgs::TwistStamped setpoint_velocity SET_POSITION_LOCAL_POSITION_NED position_setpoint_triplet
/mavros/trajectory/generated (mission) mavros_msgs::Trajectory trajectory TRAJECTORY_REPRESENTATION_WAYPOINT vehicle_trajectory_waypoint
/mavros/obstacle/send sensor_msgs::LaserScan obstacle_distance OBSTACLE_DISTANCE obstacle_distance
/mavros/set_mode mavros_msgs::SetMode sys_status SET_MODE vehicle_command

PX4 and global planner

This is the complete message flow from PX4 Firmware to the global planner.

PX4 topic MAVLink MAVROS Plugin ROS Msgs. Topic
vehicle_local_position LOCAL_POSITION_NED local_position geometry_msgs::PoseStamped mavros/local_position/pose
vehicle_local_position LOCAL_POSITION_NED local_position geometry_msgs::TwistStamped mavros/local_position/velocity

This is the complete message flow to PX4 Firmware from the global planner.

ROS topic ROS Msgs. MAVROS Plugin MAVLink PX4 Topic
/mavros/setpoint_position/local (offboard) geometry_msgs::PoseStamped setpoint_position SET_POSITION_LOCAL_POSITION_NED position_setpoint_triplet


Fork the project and then clone your repository. Create a new branch off of master for your new feature or bug fix.

Please, take into consideration our coding style. For convenience, you can install the commit hooks which will run this formatting on every commit. To do so, run ./tools/set_up_commit_hooks from the main directory.

Commit your changes with informative commit messages, push your branch and open a new pull request. Please provide ROS bags and the Autopilot flight logs relevant to the changes you have made.

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