Autonomous mobile robots are undergoing an impressive growth. They are successfully used in many different contexts ranging from service robots to autonomous vehicles. These robots are expected to move inside the environment and, in general, to perform some operation autonomously. Their reliability strongly depends on their capability to accommodate the uncertainty generated by their interaction with the physical world. The core functionality for every autonomous mobile robots is the ability to navigate autonomously inside a known environment. The navigation task can be decomposed in identify where to go, plan and follow the route to reach the goal. In order to follow the planned path the robot needs to accommodate the actuation noise. To accommodate these noise the knowledge of the pose and speed of the robot inside the environment is needed. The more accurate the localization of the robot, the better the actuation error can be compensated for. Localisation is the process of establishing the correspondence between a given map coordinate system and the robot local coordinate system relying on its perceptions of the environment and its motion. Sensors are affected by noise, and in time, ego-motion estimation alone diverges from the robot's true pose. Robot exteroceptive sensors can give fundamental information to reset the pose uncertainty and relocalise the robot inside the environment, hence mitigating the dead-reckoning process. Most of the localization systems presented in the state-of-the-art focus on the maximization of the localization accuracy by leveraging the natural features of the environment. In these systems, the maximum achievable accuracy is tightly coupled with the perceivable information embedded in the different regions of the environment. Therefore, the localization uncertainty cannot be adapted to the level of accuracy desired by the users and only few approaches can provide guarantees on the localization performance. In contrast, by infrastructuring the environment, it is possible to obtain a desired level of uncertainty. Current approaches tend to over-design the infrastructure in dimension and supported measurement frequency. They provide far more accuracy than required in most areas of the environment in order to guarantee the tightest constraints that often are required only in limited regions. The ability to adapt to the location-dependent uncertainty is more than just a desirable property for a localisation system, since it helps in the reduction of the system consumption, in the minimization of external infrastructures and in the relaxation of the assumptions to be made on the environment. In line with the considerations above, localisation throughout this thesis is not seen as the process that always has to maximise the accuracy of the estimated robot pose. On the contrary, localisation is considered as the process that minimises an objective function related to the infrastructure’s cost, to the power consumption and to the computation time, being subject to some requirements on the localization accuracy.

The development in wireless technology, mobile smart devices and Internet of Things has gave birth to a booming era or the wireless indoor geolocation. This technology have been increasingly used within our daily life and help people to build up the tracking system which could be used by fulfillment centers and grocery stores. To achieve higher localization accuracy with wireless geolocation, we need a higher density of deployment which involves high deployment and maintenance cost. To balance the accuracy and the cost, people have begun using wireless localization employing inertial navigation system (INS) which provide speed and direction of movement. When we combine Radio Frequency (RF) localization with INS, we have a hybrid INS/RF localization system which can achieve high localization accuracy with low cost. In this thesis, we use accelerometers and magnetometers in an Android smart phone to build a hybrid INS/RF system and use two different technologies for RF localization: Radio Frequency Identification Device (RFID) and Wi-Fi. Using this system, we conducted measurements of the hybrid localization system and evaluate its performance. The specific contributions of the thesis are: (1)Empirical performance evaluation of the INS/RFID localization system. It relates the localization error to the number and position of RFID tags. (2)Model the effect of metallic objects on accuracy of magnetometer. The model shows the relation between direction error and distance to metallic component. (3)Model shadow fading in close proximity of RF transmitter. It builds a distance dependent shadow fading model. (4)Model based performance evaluation of hybrid localization. The test bench uses our models to simulate the hybrid localization data.

The ability to identify where sound is coming from is required for everyday listening tasks such as identifying in which direction the phone is ringing and to help locate who is calling your name in a social situation. While this localization ability has been found to be reduced in listeners with a hearing loss in the typically measured frequency range of 250 to 8000 kHz, less is known about listeners who have a hearing loss that is mainly limited to the extended high frequencies of 8 to 14 kHz, particularly when abilities are tested with speech stimuli. The purpose of the current study was to determine whether listeners with a hearing impairment at these higher frequencies performed less accurately in a number of localization tasks. Twenty-three participants with normal hearing (thresholds not exceeding 20 dB HL from 250 to 14 kHz) and 23 participants with normal hearing up to and including 3 kHz and with at least a moderate hearing loss in the extended high frequencies (thresholds reaching at least 55 dB HL in any of the frequencies from 8 kHz to 14 kHz), localized noise and speech stimuli at a level of 75 dBA in a free field situation. Thirteen speakers were used in four different speaker arrangements; the frontal horizontal plane, lateral horizontal plane, frontal vertical plane and lateral vertical plane. The noise stimuli included noise band-passed filtered between 300 Hz and 16 kHz, and 300 Hz and 8 kHz. Speech stimuli were individual words with strong amounts of high frequency content above 8 kHz and weak amounts of high frequency content above 8 kHz. The two types of speech stimuli were also band-passed filtered using the same filter cut-off frequencies as the noise stimuli. No significant main effect differences were found between the localization ability of the two hearing groups for any of the four experiments. However, within experiment analysis revealed in the lateral vertical plane the normal hearing group localized significantly better than the hearing loss group for both the strong and weak stimuli. Significant differences were also found across experiments with both groups of participants localizing more accurately in the frontal horizontal plane and worst in the frontal vertical plane. All participants were found to localize significantly better with the greater bandwidth of 300 Hz to 16 kHz, and also for both types of speech stimuli when compared to the noise stimuli, although post hoc analysis found that these differences were not consistent between all speaker locations.

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Mobile crowd sensing is one of the most active areas of research. Participatory sensing is part of it in which participants sense their surroundings and collaborate to accomplish a given task. The participants in reference are smartphones. We focus on location dependent tasks and a problem of role assignment. An existing work on the same de fines three types of roles for the participants: broadcasters, normal participants and location information receiver. The broadcasters and normal participants turn on their GPS while location information receivers rely on broadcasters to compute their position. The existing work provides a centralized approach which uses greedy algorithm for role assignment. We propose a sorting based algorithm which minimizes 12-25% of the time for medium and large datasets. We also modify the energy model to minimize power consumption of devices. For this we provide a scheme so that only few devices turn on cellular network to contact server as cellular network consumes considerable energy of smartphones. In the existing approach if new devices join the region then they cannot participate in the ongoing sensing task until server assigns them role during the next localization phase. In addition to this, if device leaves the region then its neighbouring devices may minimize energy needs by changing their role. However, in the current work the algorithm is required to run over entire set of participants for each insertion and deletion of participant. We provide an alternative method to allocate roles adaptively to new participants and change roles for the existing devices when some devices leave the region on fly. This helps to minimize over 95-99.9% time for role assignment compared to existing state of work. In addition to this, we have also proposed a distributed approach so that devices are self-capable of assigning role to themselves based on local information. This is fi rst work so far to relieve server from the task of role assignment. Besides proposing a method, we have also taken into account the residual energy of smartphones for assigning the role of broadcaster which has not been considered before. Our algorithm takes 70-85% less time compared to centralized approach but consumes 12-15% more energy as it does not provide optimal set of broadcasters which requires global information. All the work has been validated through extensive experiments using both real and synthetic datasets.

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The venerable phenomena of Anderson localization, along with the more recent many-body localization (MBL), both depend crucially on the presence of disorder. Here we introduce a family of simple translationally invariant models of fermions locally coupled to spins, which have a disorder-free mechanism for localization. This mechanism is due to a local $\mathbb{Z}_2$ gauge symmetry and we uncover the connection to lattice gauge theories. We diagnose the localization through long-time memory of initial conditions after a global quantum quench. One of the defining features of the models that we study is the binary nature of the emergent disorder, related to the $\mathbb{Z}_2$ degrees of freedom. This results in a qualitatively different behaviour in the strong effective disorder limit compared to typically studied models of localization. For example it gives rise to the possibility of a delocalization transition via quantum percolation in higher than one dimension. In connection to the recently proposed quantum disentangled liquid (QDL) we also study the entanglement properties of our models. The QDL provides an alternative to both complete localization and to the eigenstate thermalization hypothesis. Our models highlight the subtlety of defining a QDL and we offer new insights into their entanglement properties. While the simplest models we consider can be mapped onto free fermions, we also include interactions which leads to MBL-like behaviour characterised by logarithmic entanglement growth. We further consider interactions that generate dynamics for the conserved charges, which give rise to only transient localization behaviour, or quasi-MBL. Finally, we present a proposal for the experimental measurement of gauge field correlators for our model in two-dimensions. This proposal is based on interferometric techniques which are feasible using current experimental capabilities. Furthermore, the interacting generalizations of our models can be similarly implemented in experiments, providing access to the dynamics of strongly interacting lattice gauge theories, beyond what can be simulated on a classical computer.

Title: Robot Localization Author: RNDr. David Obdržálek Department: Department of Software Engineering Supervisor: Prof. RNDr. Jaroslav Pokorný, CSc., Department of Software Engi- neering Abstract: Localization is one of the most important areas in low-level support for successful robot operations. This work presents several localization methods which have been also used for practical implementation on real robots. Among others, Monte Carlo Localization (MCL) was selected for implementation. It represents very successful method of probability localization in situations when data acquired from the sensors does not fully correspond to the reality. The resulting localization technique was successfully used for localization of several robots. The work also shows how a cheap low-end GPS receiver module can be used for localization on a graph-based map. Keywords: robotics, localization, GPS, Monte Carlo Localization

Unmanned vehicles being used more and more for tasks that need to be done in environ- ments that are hard to access, or dangerous for humans. Because the vehicles are unmanned they need some way of conveying information to the operator about where it is located. In some cases visual feedback to the operator might be enough, but in environments with low visibility other techniques are required. This thesis will address the issue of localization in an underwater environment by means of side-scan sonars and an inertial measurement unit (IMU). It will explore whether it is possible to localize a remotely operated vehicle (ROV) in a known environment by fusing data from the different sensors. A particle filter is applied to the translational motion of the ROV and an extended kalman filter is used to estimate the vehicles attitude. The focus of the thesis lies in statistical mod- eling and simulation of the ROV and its sensors rather than in validation and testing in the physical realm. Results show that a particle filter localization is plausible in environments given varied enough readings. For cases where measurements are similar, such as close to the floor of a pool the filter tends to diverge.