Received signal strength indicator (RSSI)-based fingerprinting is a widely used technique for indoor localization, but these methods suffer from high error rates due to various reflections, interferences, and noises. The use of disturbances in the magnetic field in indoor localization methods has gained increasing attention in recent years, since this technology provides stable measurements with low random fluctuations. In this paper, a novel fingerprinting-based indoor 2D positioning method, which utilizes the fusion of RSSI and magnetometer measurements, is proposed for mobile robots. The method applies multilayer perceptron (MLP) feedforward neural networks to determine the 2D position, based on both the magnetometer data and the RSSI values measured between the mobile unit and anchor nodes. The magnetic field strength is measured on the mobile node, and it provides information about the disturbance levels in the given position. The proposed method is validated using data collected in two realistic indoor scenarios with multiple static objects. The magnetic field measurements are examined in three different combinations, i.e., the measurements of the three sensor axes are tested together, the magnetic field magnitude is used alone, and the Z-axis-based measurements are used together with the magnitude in the X-Y plane. The obtained results show that significant improvement can be achieved by fusing the two data types in scenarios where the magnetic field has high variance. The achieved results show that the improvement can be above 35% compared to results obtained by utilizing only RSSI or magnetic sensor data.

Nowadays, an increasing usage of autonomous mobile robots in outdoor applications can be noticed. Identification of the terrain type is very important for efficient navigation. In this paper, a novel method is proposed for terrain classification in the case of wheeled mobile robots. The classification algorithm uses frequency domain features, which are extracted in fixed-size windows, and Multi-Layer Perceptron (MLP) neural networks as classifiers. Data from inertial sensors were collected for different outdoor terrain types using a prototype measurement system. The data of the accelerometer and the gyroscope were tested separately and together, and different processing window sizes were also applied. The achieved results show that above 99% classification efficiency can be achieved using the collected data.

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