Simulated testbed

We propose to use a simulated 500x500 pixels 2D sonar image as a cost function for testing the DFM algorithm. We want the cost function to be binary. It implies that we need to build a sonar image in which obstacles are supposed to be classified.

Before building the sonar image (SI), a binary map of obstacles (MO) is randomly generated. Three parameters control:

• the number of obstacles to generate (nbObst = 50),

• the number of obstacles to modify (nbObstMod = 15),

• the range of the width wObst and the length lObst of obstacles (10 < wObst, lObst < 100 (in pixels)).

The number of obstacles to be modified refers to the number of obstacles that will be randomly added or deleted from one map to another between the first and the second computation of the DFM algorithm in the tests of the next section.

Fig. 5. Close-up on the computation of a simulated sonar image. a) A radial sweep is performed on a binary cost map using a virtual sonar beam (blue lines). b) The sonar echoes are interpreted to build the sonar image.

Once the map of obstacles has been generated, a ray tracing technique is used to build the sonar image. A radial sweep of 360 degrees is performed using a virtual sonar beam with a limited range (sonar-range = 150 pixels). It is assumed that obstacles have the properties of total reflection and homogeneous diffraction at the virtual frequency of the sonar, so that, when the beam meets an obstacle in MO, a spot is generated in SI (see figure 5). The size of the spot, which corresponds to the duration of the sonar pulse, is tuneable (size-spot = 10 pixels)

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