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I've recently used aruco_detect successfully. It worked well first time using the DICT_5X5_50
dictionary. (I only needed a few landmarks.) The only problem I had was that it needed to run on a Raspberry Pi. aruco_detect
(or the underlying library in OpenCV) could only process about 1fps on the RPi. I used the message_filters
package to drop image frame rate down to 1fps before using the detector. (If the frame rate is too fast for aruco_detect
, the node eventually used enough memory – presumably for message buffering – to hang the RPi.)