![\"\"](\"https:\/\/peterklemperer.com\/blog\/wp-content\/uploads\/2017\/11\/Cube-Board-300x174.png\")
<\/p>\nI am using Aruco tags to locate mobile robots relative to one another. I have mounted tags on the robots using cubes so that at least one tag will be visible from every side of the robot.<\/p>\n
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In the picture above, the origin is indicated by the red, green, and blue lines overplayed over the video frame.<\/p>\n
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In the past, I had been locating each of the tags individually, but the Aruco library offers a higher precision alternative: Aruco tag boards. Aruco tag boards consist of multiple Aruco tags. The position of the tags is defined in three dimensions a-priori<\/em>, so when multiple tags appear in a frame, a better estimate of the tag boards location can be calculated than using just one single Aruco tag alone.<\/p>\n This post presents information on how I:<\/p>\n Aruco boards require three parameter: objPoints, dictionary, and ids. To learn how these variables are formatted, I used the GridBoard_create method to generate an example.\u00a0Following is the contents of those variables for:<\/p>\n For my 3-D board:<\/p>\n In this case, the model was defined by hand. For more complicated three-dimensional shapes, I will use CAD tools to calculate the tag corners.<\/p>\n After defining the board, I was able to estimate the pose of the board as follow:<\/p>\n As shown in the picture above, the individual markers are outlined and labeled with their respective id numbers. The axis are drawn on from the origin, with x-axis red, y-axis green, and z-axis blue.<\/p>\n Resources:<\/p>\n I am using Aruco tags to locate mobile robots relative to one another. I have mounted tags on the robots using cubes so that at least one tag will be visible from every side of the robot. In the picture … Continue reading \n
Learning to Define Aruco Boards<\/strong><\/h3>\n
board = aruco.GridBoard_create(2,2,5,5,\r\n aruco.getPredefinedDictionary(aruco.DICT_6X6_250))\r\n\r\nboard.objPoints =\u00a0\r\n[array([[ 0., 15., 0.],\r\n [ 5., 15., 0.],\r\n [ 5., 10., 0.],\r\n [ 0., 10., 0.]], dtype=float32), \r\narray([[ 10., 15., 0.],\r\n [ 15., 15., 0.],\r\n [ 15., 10., 0.],\r\n [ 10., 10., 0.]], dtype=float32), \r\narray([[ 0., 5., 0.],\r\n [ 5., 5., 0.],\r\n [ 5., 0., 0.],\r\n [ 0., 0., 0.]], dtype=float32), \r\narray([[ 10., 5., 0.],\r\n [ 15., 5., 0.],\r\n [ 15., 0., 0.],\r\n [ 10., 0., 0.]], dtype=float32)]\r\n\r\nboard.ids =\r\narray([[0],\r\n [1],\r\n [2],\r\n [3]], dtype=int32)<\/pre>\n
Defining My Cubic Aruco Board<\/strong><\/h3>\n
board_corners = [np.array([[0.09,0.01,0.0],[0.09,0.09,0.],[0.01,0.09,0.],[0.01,0.01,0.]],dtype=np.float32),\r\n np.array([[0.09,0.10,0.01],[0.09,0.10,0.09],[0.01,0.10,0.09],[0.01,0.10,0.01]],dtype=np.float32),\r\n np.array([[0.09,0.0,0.09],[0.09,0.0,0.01],[0.01,0.0,0.01],[0.01,0.0,0.09]],dtype=np.float32),\r\n np.array([[0.09,0.09,0.10],[0.09,0.01,0.10],[0.01,0.01,0.10],[0.01,0.09,0.10]],dtype=np.float32)]\r\nboard_ids = np.array( [[0],[10],[14],[12]], dtype=np.int32)\r\nboard = aruco.Board_create( board_corners,\r\n aruco.getPredefinedDictionary(aruco.DICT_6X6_250),\r\n board_ids )<\/pre>\n
Displaying an Aruco Board Pose<\/h2>\n
corners, ids, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict)\r\nretval,rvec,tvec = aruco.estimatePoseBoard( corners, ids, board, camera_matrix, dist_coeffs )\r\nframe = aruco.drawAxis( frame, camera_matrix, dist_coeffs, rvec, tvec, aruco_marker_length_meters )\r\nframe = aruco.drawDetectedMarkers( frame, corners, ids )<\/pre>\n
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