ROS - ZED Node To start a ZED ROS node you can use the following commands in a shell console: ZED: $ roslaunch zed_wrapped zed.launch ZED Mini: $ roslaunch zed_wrapped zedm.launch ZED 2: $ roslaunch zed_wrapped zed2.launch Published Topics The ZED node publishes data to the following topics: Left camera rgb/image_rect_color: Color rectified image (left RGB image by default) rgb/image_rect_gray: Grayscale rectified image (left RGB image by default) rgb_raw/image_raw_color: Color unrectified image (left RGB image by default) rgb_raw/image_raw_gray: Grayscale unrectified image (left RGB image by default) rgb/camera_info: Color camera calibration data rgb_raw/camera_info: Color unrectified camera calibration data left/image_rect_color: Left camera color rectified image left/image_rect_gray: Left camera grayscale rectified image left_raw/image_raw_color: Left camera color unrectified image left_raw/image_raw_gray: Left camera grayscale unrectified image left/camera_info: Left camera calibration data left_raw/camera_info: Left unrectified camera calibration data Right camera right/image_rect_color: Color rectified right image right_raw/image_raw_color: Color unrectified right image right/image_rect_gray: Grayscale rectified right image right_raw/image_raw_gray: Grayscale unrectified right image right/camera_info: Right camera calibration data right_raw/camera_info: Right unrectified camera calibration data Stereo pair stereo/image_rect_color: stereo rectified pair images side-by-side stereo_raw/image_raw_color: stereo unrectified pair images side-by-side Note: to retrieve the camera parameters you can subscribe to the topics left/camera_info, right/camera_info, left_raw/camera_info and right_raw/camera_info Depth and point cloud depth/depth_registered: Depth map image registered on left image (32-bit float in meters by default) depth/camera_info: Depth camera calibration data point_cloud/cloud_registered: Registered color point cloud confidence/confidence_map: Confidence image (floating point values to be used in your own algorithms) disparity/disparity_image: Disparity image Tracking odom: Absolute 3D position and orientation relative to the Odometry frame (pure visual odometry for ZED, visual-inertial for ZED-M and ZED 2) pose: Absolute 3D position and orientation relative to the Map frame (Sensor Fusion algorithm + SLAM + Loop closure) pose_with_covariance: Camera pose referred to Map frame with covariance path_odom: Sequence of camera odometry poses in Map frame path_map: Sequence of camera poses in Map frame Mapping mapping/fused_cloud: Fused color point cloud Note: published only if mapping is enabled, see mapping/mapping_enabled parameter Sensor Data imu/data: Accelerometer, gyroscope, and orientation data in Earth frame [only ZED-M and ZED 2] imu/data_raw: Accelerometer and gyroscope data in Earth frame [only ZED-M and ZED 2] imu/mag: Calibrated magnetometer data [only ZED 2] atm_press: Atmospheric pressure data [only ZED 2] temperature/imu: Temperature of the IMU sensor [only ZED 2] temperature/left: Temperature of the left camera sensor [only ZED 2] temperature/right: Temperature of the right camera sensor [only ZED 2] left_cam_imu_transform: Transform from left camera sensor to IMU sensor position Object Detection [only ZED 2] objects: array of the detected/tracked objects for each camera frame [only ZED 2] object_markers: array of markers of the detected/tracked objects to be rendered in Rviz [only ZED 2] Diagnostic /diagnostics: ROS diagnostic message for ZED cameras ZED parameters You can specify the parameters to be used by the ZED node modifying the values in the files params/common.yaml: common paramters to all camera models params/zed.yaml: parameters for the ZED camera params/zedm.yaml: parameters for the ZED Mini camera params/zed2.yaml: parameters for the ZED 2 camera General parameters Parameters with prefix general Parameter Description Value camera_name A custom name for the ZED camera. Used as namespace and prefix for camera TF frames string, default=zed camera_model Type of Stereolabs camera zed: ZED, zedm: ZED-M, zed2: ZED 2 camera_flip Flip the camera data if it is mounted upsidedown true, false zed_id Select a ZED camera by its ID. Useful when multiple cameras are connected. ID is ignored if an SVO path is specified int, default 0 serial_number Select a ZED camera by its serial number int, default 0 resolution Set ZED camera resolution 0: HD2K, 1: HD1080, 2: HD720, 3: VGA frame_rate Set ZED camera video framerate int gpu_id Select a GPU device for depth computation int, default -1 (best device found) base_frame Frame_id of the frame that indicates the reference base of the robot string, default=base_link verbose Enable/disable the verbosity of the SDK true, false svo_compression Set SVO compression mode 0: LOSSLESS (PNG/ZSTD), 1: H264 (AVCHD) ,2: H265 (HEVC) self_calib Enable/disable self calibration at starting true, false Video parameters Parameters with prefix video Parameter Description Value img_downsample_factor Resample factor for images [0.01,1.0]. The SDK works with native image sizes, but publishes rescaled image. double, default=1.0 extrinsic_in_camera_frame If false extrinsic parameter in camera_info will use ROS native frame (X FORWARD, Z UP) instead of the camera frame (Z FORWARD, Y DOWN) [true use old behavior as for version < v3.1] true, false Depth parameters Parameters with prefix depth Parameter Description Value quality Select depth map quality 0: NONE, 1: PERFORMANCE, 2: MEDIUM, 3: QUALITY, 4: ULTRA sensing_mode Select depth sensing mode (change only for VR/AR applications) 0: STANDARD, 1: FILL depth_stabilization Enable depth stabilization. Stabilizing the depth requires an additional computation load as it enables tracking 0: disabled, 1: enabled openni_depth_mode Convert 32bit depth in meters to 16bit in millimeters 0: 32bit float meters, 1: 16bit uchar millimeters depth/depth_downsample_factor Resample factor for depth data matrices [0.01,1.0]. The SDK works with native data sizes, but publishes rescaled matrices (depth map, point cloud, …) double, default=1.0 min_depth Minimum value allowed for depth measures Min: 0.3 (ZED) or 0.1 (ZED-M), Max: 3.0 - Note: reducing this value will require more computational power and GPU memory. In cases of limited compute power, increasing this value can provide better performance max_depth Maximum value allowed for depth measures Min: 1.0, Max: 30.0 - Values beyond this limit will be reported as TOO_FAR Position parameters Parameters with prefix pos_tracking Parameter Description Value publish_tf Enable/disable publish TF frames true, false publish_map_tf Enable/disable publish map TF frame true, false map_frame Frame_id of the pose message string, default=map odometry_frame Frame_id of the odom message string, default=odom area_memory_db_path Path of the database file for loop closure and relocalization that contains learnt visual information about the environment string, default=`` pose_smoothing Enable smooth pose correction for small drift correction 0: disabled, 1: enabled area_memory Enable Loop Closing true, false floor_alignment Indicates if the floor must be used as origin for height measures true, false initial_base_pose Initial reference pose vector, default=[0.0,0.0,0.0, 0.0,0.0,0.0] -> [X, Y, Z, R, P, Y] init_odom_with_first_valid_pose Indicates if the odometry must be initialized with the first valid pose received by the tracking algorithm true, false path_pub_rate Frequency (Hz) of publishing of the trajectory messages float, default=2.0 path_max_count Maximum number of poses kept in the pose arrays (-1 for infinite) int, default=-1 Mapping parameters Parameters with prefix mapping Note: the mapping module requires SDK v2.8 or higher Parameter Description Value mapping_enabled Enable/disable the mapping module true, false resolution Resolution of the fused point cloud [0.01, 0.2] double, default=0.1 max_mapping_range Maximum depth range while mapping in meters (-1 for automatic calculation) [2.0, 20.0] double, default=-1 fused_pointcloud_freq Publishing frequency (Hz) of the 3D map as fused point cloud double, default=1.0 Sensors parameters (only ZED-M and ZED 2) Parameters with prefix sensors Parameter Description Value sensors_timestamp_sync Synchronize Sensors message timestamp with latest received frame true, false Object Detection parameters (only ZED 2) Parameters with prefix object_detection Parameter Description Value od_enabled Enable/disable the Object Detection module true, false confidence_threshold Minimum value of the detection confidence of an object int [0,100] object_tracking_enabled Enable/disable the tracking of the detected objects true, false people_detection Enable/disable the detection of persons true, false vehicle_detection Enable/disable the detection of vehicles true, false Dynamic parameters The ZED node lets you reconfigure these parameters dynamically: Parameter Description Value general/pub_frame_rate Frequency of the publishing of Video and Depth images (equal or minor to grab_frame_rate value) float [0.1,60.0] depth/depth_confidence Threshold to reject depth values based on their confidence. Each depth pixel has a corresponding confidence. A lower value means more confidence and precision (but less density). An upper value reduces filtering (more density, less certainty). A value of 100 will allow values from 0 to 100. (no filtering). A value of 90 will allow values from 10 to 100. (filtering lowest confidence values). A value of 30 will allow values from 70 to 100. (keeping highest confidence values and lowering the density of the depth map). The value should be in [1,100]. By default, the confidence threshold is set at 100, meaning that no depth pixel will be rejected. int [0,100] depth/depth_texture_conf Threshold to reject depth values based on their textureness confidence. A lower value means more confidence and precision (but less density). An upper value reduces filtering (more density, less certainty). The value should be in [1,100]. By default, the confidence threshold is set at 100, meaning that no depth pixel will be rejected. int [0,100] depth/point_cloud_freq Frequency of the pointcloud publishing (equal or minor to frame_rate value) float [0.1,60.0] video/brightness Defines the brightness control int [0,8] video/contrast Defines the contrast control int [0,8] video/hue Defines the hue control int [0,11] video/saturation Defines the saturation control int [0,8] video/sharpness Defines the sharpness control int [0,8] video/gamma Defines the gamma control int [1,9] video/auto_exposure_gain Defines if the Gain and Exposure are in automatic mode or not true, false video/gain Defines the gain control [only if auto_exposure_gain is false] int [0,100] video/exposure Defines the exposure control [only if auto_exposure_gain is false] int [0,100] video/auto_whitebalance Defines if the White balance is in automatic mode or not true, false video/whitebalance_temperature Defines the color temperature value (x100) int [42,65] To modify a dynamic parameter, you can use the GUI provided by the rqt stack: $ rosrun rqt_reconfigure rqt_reconfigure Transform frames The ZED ROS wrapper broadcasts multiple coordinate frames that each provide information about the camera’s position and orientation. If needed, the reference frames can be changed in the launch file. base_frame is the current position and orientation of the reference base of the robot <camera_name>_camera_center is the current position and orientation of ZED, determined by visual odometry and the tracking module <camera_name>_right_camera is the position and orientation of the ZED’s right camera <camera_name>_right_camera_optical is the position and orientation of the ZED’s right camera optical frame <camera_name>_left_camera is the position and orientation of the ZED’s left camera <camera_name>_left_camera_optical is the position and orientation of the ZED’s left camera optical frame <camera_name>_imu_link is the origin of the inertial data frame (ZED-M and ZED 2only) <camera_name>_mag_link is the origin of the magnetometer frame (ZED 2only) <camera_name>_baro_link is the origin of the barometer frame (ZED 2only) <camera_name>_temp_left_link is the origin of the left temperature frame (ZED 2only) <camera_name>_temp_right_link is the origin of the right temperature frame (ZED 2only) For RVIZ compatibilty, the root frame map_frame is called map. The TF tree generated by the zed_wrapper reflects the standard descripted in REP105. The odometry frame is updated using only the “visual odometry” information. The map frame is updated using the Tracking algorithm provided by the Stereolabs SDK, fusing the inertial information from the IMU sensor if using a ZED-M camera. map_frame (`map`) └─odometry_frame (`odom`) └─base_frame (`base_link`) └─camera_frame (`<camera_name>_camera_center`) | └─left_camera_frame (`<camera_name>_left_camera_frame`) | | └─left_camera_optical_frame (`<camera_name>_left_camera_optical_frame`) | | └─left_temperature:frame (`<camera_name>_temp_left_link`) | └─right_camera_frame (`<camera_name>_right_camera_frame`) (*only ZED 2*) | └─right_camera_optical_frame (`<camera_name>_right_camera_optical_frame`) | └─left_temperature:frame (`<camera_name>_temp_left_link`) └─imu_frame (`<camera_name>_imu_link`) (*only ZED-M and ZED 2*) └─magnetometer_frame (`<camera_name>_mag_link`) (*only ZED 2*) └─barometer_frame (`<camera_name>_baro_link`) (*only ZED 2*) ZED-M The ZED-M provides the same information as the ZED, plus the inertial data from the IMU sensor. The IMU data are used internally to generate the pose in the Map frame with the Tracking sensor fusion algorithm. Note: The initial pose in Odometry frame can be set to the first pose received by the Tracking module by setting the parameter init_odom_with_first_valid_pose to true. ZED 2 The ZED 2 provides the same information as the ZED and the ZED-M, plus the data from a new set of sensors. The ZED 2 provides magnetometer data that are used internally by the SDK to get better and absolute information about the YAW angle, atmospheric pressure information to be used to estimate the height of the camera respect to a reference point and temperature information to check the camera health in a complex robotic system. Services The ZED node provides the following services: reset_tracking: Restarts the Tracking module setting the initial pose to the value available in the param server reset_odometry: Resets the odometry values eliminating the drift due to the Visual Odometry algorithm, setting the new odometry value to the latest camera pose received from the tracking module set_pose: Sets the current pose of the camera to the value passed as single parameters -> X, Y, Z [m], R, P, Y [rad] set_led_status: Sets the status of the blue led -> True: LED ON, False: LED OFF (At least FW 1523 and SDK v2.8 is required) toggle_led: Toggles the status of the blue led, returning the new status (At least FW 1523 and SDK v2.8 is required) start_svo_recording: Starts recording an SVO file. If no filename is provided the default zed.svo is used. If no path is provided with the filename the default recording folder is ~/.ros/ stop_svo_recording: Stops an active SVO recording start_remote_stream: Starts streaming over network to allow processing of ZED data on a remote machine. See Remote streaming stop_remote_stream: Stops streaming over network start_3d_mapping: Starts the Spatial Mapping processing. See Spatial Mapping stop_3d_mapping: Stops the Spatial Mapping processing (works even with automatic start from configuration file) start_object_detection: Starts the Object Detection processing. See Object Detection Note: returns error if not using a ZED 2 camera stop_3d_mapping: Stops the Object Detection processing (works even with automatic start from configuration file) Note: Currently the H26x SVO compression uses the resolution and the real FPS to calculate the bitrate. This feature can lead to some issues of quality when FPS is low. On Nvidia Jetson TX1 and Jetson TX2, the FPS is quite low at the beginning of the grab loop, therefore it could be better to wait for some grab calls before calling start_svo_recording so that the camera FPS is stabilized at the requested value (15fps or more). Remote streaming With SDK v2.8 has been introduced the capability to acquire ZED data and to stream it on a remote machine over network. This features is useful when the ZED is connected to a machine with limited CUDA capabilities while a high definition analysis is required. Starting the streaming without activating any other feature (i.e. depth processing, positional tracking, point cloud publishing, …) requires only the power for H264 or H265 compression. To start streaming the service start_remote_stream must be called with the following parameters: codec (def. 0): Defines the codec used for streaming (0: AVCHD [H264], 1: HEVC [H265]) Note: If HEVC (H265) is used, make sure the recieving host is compatible with HEVC decoding (basically a pascal NVIDIA card). If not, prefer to use AVCHD (H264) since every compatible NVIDIA card supports AVCHD decoding port (def. 30000): Defines the PORT the data will be streamed on. Note: port must be an even number. Any odd number will be rejected uint16 port=30000 bitrate (def. 2000): Defines the streaming BITRATE in Kbits/s gop_size (def. -1): Defines the GOP SIZE in frame unit. Note: if value is set to -1, the gop size will match 2 seconds, depending on camera fps. The gop size determines the maximum distance between IDR/I-frames. Very high GOP size will result in slightly more efficient compression, especially on static scene. But it can result in more latency if IDR/I-frame packet are lost during streaming. Maximum allowed value is 256 (frames) adaptative_bitrate (def. False): Enable/Disable adaptive bitrate. Note: Bitrate will be adjusted regarding the number of packet loss during streaming. If activated, bitrate can vary between [bitrate/4, bitrate]. Currently, the adaptive bitrate only works when “sending” device is a NVIDIA jetson (X1,X2,Xavier,Nano) Receive a remote stream To acquire the streaming on a remote machine start a ZED node with the following command: $ roslaunch zed_wrapper zed.launch stream:=<sender_IP>:<port> For example: $ roslaunch zed_wrapper zed.launch stream:=192.168.1.127:30000 Note: the stream contains only visual information. Using a ZED-M camera the inertial topic will not be available if not subscribed using ROS standard methods. Spatial Mapping The ZED node provides a basilar mapping module publishing a 3D map of the environment as 3D color point cloud (mapping/fused_cloud). Spatial Mapping is disabled by default as it’s an heavy consuming process, it can be enabled setting to true the parameter mapping/mapping_enabled in the file params/common.yaml. Spatial Mapping can be enabled manually at any time using the start_3d_mapping service. To reduce the computational power requirements use value higher than 0.1 m for resolution_m and decrease the value of fused_pointcloud_freq to reduce the frequency of the point cloud topic publishing. Object Detection Using the ZED 2 camera unlocks the Object Detection module that allows to detect and track Persons and Vehicles. Object detection is disabled by default as it’s an heavy consuming process, it can be automatically enabled when the node starts setting to true the parameter object_detection/od_enabled in the file params/zed2.yaml. If Object Detection is disabled it can be manually enabled at any time using the start_object_detection service, with the parameters confidence_threshold [0,100],object_tracking_enabled [True,False],people_detection [True,False] andvehicle_detection [True,False]. See Object Detection parameters for more info about the meaning of each parameter. Object Tracking Enabling Object Tracking allows to get information frame by frame about the status of each detected object. The same object is identified with the same ID in all the frames after its first detection and its position is estimated if an occlusion occurs. Each detected object can get four different values about their Tracking Status: OFF - The tracking is not yet initialized, the object ID is not usable OK - The object is tracked SEARCHING - The object couldn’t be detected in the image and is potentially occluded, the trajectory is estimated TERMINATE - This is the last searching state of the track, the track will be deleted in the next retreiveObject Custom message topics The ZED node publishes the list of the detected objects using two custom messages: object_stamped.msg: the single detected object with the timestamp objects.msg: the array of all the objects detected/tracked in a single frame object_stamped.msg # Standard Header Header header # Object label string label # Object label ID int16 label_id # Object confidence level (1-99) float32 confidence # Object centroid geometry_msgs/Point32 position # Object velocity geometry_msgs/Vector3 linear_vel # Tracking status # 0 -> OFF - The tracking is not yet initialized, the object ID is not usable # 1 -> OK - The object is tracked # 2 -> SEARCHING - The object couldn't be detected in the image and is potentially occluded, the trajectory is estimated # 3 -> TERMINATE - This is the last searching state of the track, the track will be deleted in the next retreiveObject int8 tracking_state # 2D Bounding box projected to Camera image # 0 ------- 1 # | | # | | # | | # 3 ------- 2 geometry_msgs/Point32 bbox_2d # 3D Bounding box in world frame # 1 ------- 2 # /. /| # 0 ------- 3 | # | . | | # | 5.......| 6 # |. |/ # 4 ------- 7 geometry_msgs/Point32 bbox_3d objects.msg # Array of `object_stamped` topics object_stamped objects Diagnostic The ZED node publishes diagnostic information that can be used by the robotics system using a diagnostic_aggregator node. Using the Runtime monitor plugin of rqt it is possible to get all the diagnostic information and check that the node is working as expected: A brief explanation of each field: Component: name of the diagnostic component Message: summary of the status of the ZED node HardwareID: Model of the ZED camera and its serial number Capture: grabbing frequency (if video or depth data are subscribed) and the percentage respect to the camera frame rate Processing time: time in seconds spent to elaborate data and the time limit to achieve max frame rate Playing SVO: (visible only if playng an SVO file) current frame position in the SVO file over the total frame count Depth status: indicates if the depth processing is performed Point Cloud: point cloud publishing frequency (if there is at least a subscriber) and the percentage respect to the camera frame rate Floor Detection: if the floor detection is enabled, indicates if the floor has been detected and the camera position correctly initialized Tracking status: indicates the status of the positional tracking, if enabled Object data processing: if Object Detection is enabled indicates the time required to process the data relative to detected objects IMU: the publishing frequency of the IMU topics, if the camera is the ZED Mini and there is at least a subscriber Left CMOS Temp.: (only ZED 2) the temperature of the CMOS of the left camera sensor [-273.15°C if not valid] Right CMOS Temp.: (only ZED 2) the temperature of the CMOS of the right camera sensor [-273.15°C if not valid] SVO Recording: indicates if the SVO recording is active SVO Compression time: average time spent on frame compressing SVO Compression ratio: average frame compression ratio Using multiple ZEDs It is possible to use multiple ZED cameras with ROS. Simply launch the node with the zed_multi_cam.launch file: $ roslaunch zed_wrapper zed_multi_cam.launch Assigning a GPU to a camera To improve performance, you can specify the gpu_id of the graphic card that will be used for the depth computation in the launch file. The default value (-1) will select the GPU with the highest number of CUDA cores. When using multiple ZEDs, you can assign each camera to a GPU to increase performance. Limitations Performance This wrapper lets you quickly prototype applications and interface the ZED with other sensors and packages available in ROS. However, the ROS layer introduces significant latency and a performance hit. If performance is a major concern for your application, please consider using the ZED SDK library. Multiple ZEDs The ZED camera uses the maximum bandwidth provided by USB 3.0 to output video. When using multiple ZEDs, you may need to reduce camera framerate and resolution to avoid corrupted frames (green or purple frames). You can also use multiple GPUs to load-balance computations and improve performance.