Geoplot(gx,finalGPS(:,1),finalGPS(:,2), 'r-+', 'DisplayName', 'GPS before correction') Geoplot(gx,finalSigns(:,3),finalSigns(:,4), '*', 'DisplayName', 'HDLM traffic signs') Geoplot(gx,finalSigns(:,1),finalSigns(:,2), 'o', 'DisplayName', 'Lidar traffic signs') Legend(gx, 'Boundaries', 'Nodes', 'Links', 'Location', 'northwest') % Add HERE basemap with custom attribution.Īttribution = ĪddCustomBasemap( 'herestreets',url, 'Attribution',attribution) į = figure( 'Name', 'Corrected GPS measurements') HereTokens = inputdlg(prompt,title,dims) PtClouds/256/png?apikey=%s'] ĭims = % Text edit field height and width Collect frames from other sensors by % selecting the closest ones according to their time stamps. % Since each sensor runs at a different frequency, use the % lidar's acquisition rate as the basis for synchronizing lidar, % video and GPS data. NumLidarFrames = size(bagLidar.MessageList,1) MsgStructsImage = readMessages(bagImage) % Apply the correction to the GPS coordinates reported by the GPS sensorįinalGPS = įinalSigns = ĭisplay the results of the localization pipeline on a map using the calculated latitude and longitude measurements.įunction = helperReadRosbag(filepath)īagLidar = select(bag, 'Topic',lidarTopic) īagImage = select(bag, 'Topic',imageTopic) % Once a traffic sign is not detected anymore, calculate the error between % the traffic signs detected by the lidar and camera and the traffic signs % selected from the HDLM data.Įrror = hdlmDetectedSigns(end,1:2) - mean(lidarDetectedSigns(:,1:2)) RelevantGPS = īreak end end end elseif numDetections > 1 % Transform the coordinates from the lidar frame to the GPS frame SignRefs = vertcat(roadToSignsRefs.SignRefs) RoadToSignsRefs = signLayer(m,1).RoadToSignsReferences
% Make sure that the closest traffic sign is on the closest link for m = 1:size(signLayer,1) % If a traffic sign is detected by lidar and camera, find the ID of % the traffic sign that is closest to the current GPS position.ĭistToSigns = helperDistance(gpsMeasurement(1),gpsMeasurement(2).
Load the bounding boxes of the traffic signs for both the images and point clouds.ĬlosestLinkID = helperFindClosestLink(linkData, gpsMeasurement) To simplify the processing for illustration purposes, the traffic sign data in this example has been hand-annotated using the Ground Truth Labeler app. This projection gives you the 3-D locations of the traffic signs, which can be used to calculate the error metric based on the traffic signs detected in HERE HD map data.
In a complete pipeline, you first detect the traffic signs in the camera frames, and then project them onto the lidar point clouds using the lidar-camera calibration result. This data was % extracted from a much larger rosbag collected using an autonomous car. = helperReadRosbag(filepath) Įnd % Load synchronized sensor data directly from MAT files. An entire data set is loaded below from MAT files. % helperReadRosbag uses a small rosbag sample to show how a larger % rosbag can be processed. HasROSToolbox = license( 'test', 'ros_toolbox') įilepath = ')Įnd % Extract data from a rosbag (requires ROS Toolbox) % Extract contents of the downloaded fileĭisp( 'Extracting localizationAutonomousCarDataset.zip (1.01 GB). Websave(zipFileName, baseDownloadURL, options) % Create a folder in a temporary directory to save the downloaded file if ~folderExistsĭisp( 'Downloading localizationAutonomousCarDataset.zip (1.01 GB). DataFolder = fullfile(tempdir, 'localizationAutonomousCarDataset',filesep)
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