Wide area symbol recognition is a task that plagues many autonomous vehicles. A process is needed first to recognize if the symbol is present, and if so where it is. Once the symbol's position is detected it must be analyzed and recognized. In this scenario we have a submersible attempting to locate man made objects on the bottom of a large water basin. These man made objects have bar codes on them that need to be read and the position of the code needs to be recorded relative to where it is in the entire pond. A two step process has been developed to allow the position recognition within a frame to be dealt with on a separate DSP associated with one of three total cameras. The object recognition is then dealt with on a high speed computer aboard the vehicle to read the proper code. The reading is done using a statistics based approach that assumes a noisy, but contrasting background. This approach has proven to be effective in environments in which the background has very little ordered noise, such as the bottom of lakes and ponds, but requires very high clarity in order to capture a suitable image.
There is a need for autonomous submarines that can quickly and safely complete jobs, such as the recovery of a downed aircraft's black box recorder. In order to complete this feat, it is necessary to use an optical processing algorithm that distinguishes a desired target and uses the feedback from the algorithm to retrieve the target. The algorithm itself uses many bit mask filters for particle information, and then uses a unique rectation method in order to resolve complete objects. The algorithm has been extensively tested on an AUV platform, and proven to succeed repeatedly in approximately five or more feet of water clarity.
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