Abstract
Certain obstacle mapping applications require the live evaluation of the measured data to prevent collision with obstacles. The fusion of different or similar sensors usually has a high calculation demand, which increases significantly with the area to be evaluated and the number of sensors. In the present considerations, we propose a wavelet-based adaptive optimization method, which can greatly decrease the number of grid points to be evaluated, and thus the necessary computation time. The basis of the method is to use the fact that the areas to be evaluated mostly face a rather small number of obstacles, which cover a smaller percentage of the whole environment. The first step in a pre-filtering process is the determination of the zones where no obstacles are present. This step can already result in a considerable decrease in the computation time, however with the transformation to polar coordinates, the method will not only be more fitted to the problem to be solved, but the area of the evaluation can also be increased with the same number of grid points. As a last step, we applied wavelet transformation to identify the regions of interest, where the application of a refined raster is necessary, and thus further decreasing the number of grid points where the calculation has to be carried out. We used our previously developed probability-based ultrasonic sensor fusion inverse algorithm to demonstrate the efficiency of the proposed method.
Highlights
From navigation to mapping, from target localization to obstacle avoidance, ultrasound sensors are widely used, in medicine and industry, and in everyday life
Ultrasound sensors are usually based on the measurement of the time of flight of a ping signal, but a Doppler effect can help in detecting movements
The measurement to be made if the reflected echoes from an object are to be evaluated, i.e., the indirect measurement, is used in the case of target localization, area mappings, and collision avoidance
Summary
From target localization to obstacle avoidance, ultrasound sensors are widely used, in medicine and industry, and in everyday life. Whether there is an obstacle at a given domain of the space around the sensors and transmitter, it is more convenient to create a Cartesian coordinate based grid of a given resolution, and calculate the fusion of the signals from all sensors for each of the grid points. For the ith sensor, for all the locations on the grid (xa, yb, zc), the approximation of the measured signal intensity can be calculated by interpolation This means, that if we have a measured point at each integer a, b, c, the value corresponding to the distance Siabc is: A(Siabc) = A( Siabc ) +. Different sized parts of the original measurements were evaluated, achieving a different number of evaluated points and different occupation ratios of the area The decrease in the runtime between the reference and the pre-filtered measurement in the best case was 39%, and it is statable that with the increase in the number of evaluated points, the decrease in runtime is bigger, in the case of a similar or lower occupation ratio of the space
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