In modern engineering applications, localization and orientation play an increasingly crucial role in ensuring the successful execution of assigned tasks. Industrial robots, smart home systems, healthcare environments, nuclear facilities, agriculture, and autonomous vehicles are just a few examples of fields where localization technologies are applied. Over the years, these technologies have evolved significantly, with numerous methods being developed, proposed, and refined. This paper aims to provide a comprehensive review of the primary localization and orientation technologies available in the literature, detailing the fundamental principles on which they are based and the key algorithms used to implement them. To achieve accurate and reliable localization, fusion-based approaches are often necessary, integrating data from multiple sensors and systems or estimating hidden states. For this purpose, algorithms such as Kalman Filters, Particle Filters, or Neural Networks are usually adopted. The first part of this article presents an extensive review of localization technologies, including radio frequency, RFID, laser-based systems, vision-based techniques, light-based positioning, IMU-based methods, odometry, and ultrasound-based solutions. The second part focuses on the most widely used algorithms for localization. Finally, summary tables provide an overview of the best and most consistent accuracies reported in the literature for the investigated technologies and systems.

Magnetometer-based localization is a challenging task both in indoor and outdoor applications since their reliability depends on the environmental characteristics. The obtained geomagnetic field enables the estimation of both orientation and position if proper calibration and data fusion with inertial measurement units (IMUs), GPS and radio modules are executed. However, the distortions by hard and soft iron effects of both metallic objects and building structures requires additional data processing steps to obtain usable measurement data. This paper addresses the applicability of magnetometers for indoor mobile robot localization purposes. A measurement method is elaborated, which obtains the metallic objects-related disturbance characteristics with the help of both a robotic arm-based setup, which simulates the movement of the mobile robot, and data acquisition steps for the calculation of induced effects of disturbing objects. The experimental setup is applied to investigate four scenarios, namely, measurements with one simpler disturbing object either on the right or left side of the robot, with simpler disturbing objects on both sides, and with one complex object on one side of the robot. To extract the effect of the objects, the measurements collected with no additional object are subtracted from the measurements in the case of each scenario. The obtained measurement results clearly validate that incorporating the measurements of the undisturbed scenario enables the obtainment of the effect of disturbing objects. These results form the basis for the development of intelligent fusion algorithms of magneto-inertial sensors for mobile robot localization tasks.

Mobile robot pose information is getting utilized in more and more indoor applications. In this paper, a novel fingerprinting-based positioning method is proposed. The method utilizes magnetic field data together with measurements collected using anchors equipped with multiple wireless technologies. The collected fingerprints are used to train MultiLayer Perceptron (MLP) neural networks, which can later be utilized to estimate the 2D position based on sensor readings in an unknown position. Real measurements collected in a laboratory are used to validate the performance of the method. The measurement database consists of 3D magnetometer measurements and four different parameter types measured between the mobile node and the anchor nodes using multiple wireless technologies. The parameters include Time of Flight (ToF) measurements using Ultra-WideBand (UWB) and Received Signal Strength Indicator (RSSI) measured using WiFi, UWB and 433 MHz frequency band. Various combinations of data were tested in the evaluation process. The obtained results showed that significant improvement, even more than 35%, can be achieved in the positioning performance for most combinations by fusing magnetometer measurements and wireless communication data.

Optimization is a mathematical discipline or tool suitable for minimizing or maximizing a function. It has been largely used in every scientific field to solve problems where it is necessary to find a local or global optimum. In the engineering field of localization, optimization has been adopted too, and in the literature, there are several proposals and applications that have been presented. In the first part of this article, the optimization problem is presented by considering the subject from a purely theoretical point of view and both single objective (SO) optimization and multi-objective (MO) optimization problems are defined. Additionally, it is reported how local and global optimization problems can be tackled differently, and the main characteristics of the related algorithms are outlined. In the second part of the article, extensive research about local and global localization algorithms is reported and some optimization methods for local and global optimum algorithms, such as the Gauss–Newton method, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), and so on, are presented; for each of them, the main concept on which the algorithm is based, the mathematical model, and an example of the application proposed in the literature for localization purposes are reported. Among all investigated methods, the metaheuristic algorithms, which do not exploit gradient information, are the most suitable to solve localization problems due to their flexibility and capability in solving non-convex and non-linear optimization functions.

Magnetometers are widely used in sensor fusion frameworks of mobile robots due to their ability to work as compasses. The measurements of these sensors are largely influenced by hard iron and soft iron effects that are caused by nearby ferromagnetic objects. In this paper, an artificial neural network (ANN)-based method is proposed for the identification of such effects, which can be compensated from the measurements. The method utilizes both the distance and the angle of the detected object based on the coordinate frame of the mobile robot as the inputs of the Multi-Layer Perceptron (MLP) ANN. The MLP provides on its outputs the disturbance levels in the three axes, which need to be subtracted from the measurements to obtain the compensated values. The method was tested with three different types of objects to evaluate the performance of the method. Measurements were collected using an industrial robotic arm in a grid around the objects. The disturbance levels generated by the objects were computed by subtracting measurements recorded in an undisturbed scenario. Three-layer MLPs were tested with various numbers of hidden layer neurons to find the optimal configurations. The achieved results show that the disturbance levels can be significantly decreased using the proposed method.