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.

Depth mapping can be carried out by ultrasound measuring devices using the time of flight method. Ultrasound measurements are favorable in such environments, where the light or radio frequency measurements can not be applied due to the noise level, calculation complexity, reaction time, size and price of the device, accuracy or electromagnetic compatibility. It is also usual to apply and fusion ultrasound sensors with other types of sensors to increase the precision and reliability. In the case of visually impaired people, an echolocation based aid for determining the distance and the direction of obstacles in the surroundings can improve the life quality by giving the possibility to move alone and individually in unlearnt or rapidly changing environments. In the following considerations, a model system is presented which can provide rather reliable position and distance to multiple objects. The mathematical model based on the time of flight method with a correction: it uses the measured analog sensor signals, which represent the probability of the presence of an obstacle. The device consists of multiple receivers, but only one source. The sensor fusion algorithm for this setup and the results of indoor experiments are presented. The mathematical model allows the usage, processing, and fusion of the signals of up to an infinite number of sensors. In addition, the positions of the sensors can be arbitrary, and the mathematical model does not restrict them to be placed in regular formations.