A Review of Hybrid Indoor Positioning Systems Employing WLAN Fingerprinting and Image Processing

Location-based services (LBS) are a significant permissive technology. One of the main components in indoor LBS is the indoor positioning system (IPS). IPS utilizes many existing technologies such as radio frequency, images, acoustic signals, as well as magnetic sensors, thermal sensors, optical sensors, and other sensors that are usually installed in a mobile device. The radio frequency technologies used in IPS are WLAN, Bluetooth, Zig Bee, RFID, frequency modulation, and ultra-wideband. This paper explores studies that have combined WLAN fingerprinting and image processing to build an IPS. The studies on combined WLAN fingerprinting and image processing techniques are divided based on the methods used. The first part explains the studies that have used WLAN fingerprinting to support image positioning. The second part examines works that have used image processing to support WLAN fingerprinting positioning. Then, image processing and WLAN fingerprinting are used in combination to build IPS in the third part. A new concept is proposed at the end for the future development of indoor positioning models based on WLAN fingerprinting and supported by image processing to solve the effect of people presence around users and the user orientation problem

Learning cognitive maps: Finding useful structure in an uncertain world

In this chapter we will describe the central mechanisms that influence how people learn about large-scale space. We will focus particularly on how these mechanisms enable people to effectively cope with both the uncertainty inherent in a constantly changing world and also with the high information content of natural environments. The major lessons are that humans get by with a less is more approach to building structure, and that they are able to quickly adapt to environmental changes thanks to a range of general purpose mechanisms. By looking at abstract principles, instead of concrete implementation details, it is shown that the study of human learning can provide valuable lessons for robotics. Finally, these issues are discussed in the context of an implementation on a mobile robot. © 2007 Springer-Verlag Berlin Heidelberg

Adaptive Localization and Mapping for Planetary Rovers

Future rovers will be equipped with substantial onboard autonomy as space agencies and industry proceed with missions studies and technology development in preparation for the next planetary exploration missions. Simultaneous Localization and Mapping (SLAM) is a fundamental part of autonomous capabilities and has close connections to robot perception, planning and control. SLAM positively affects rover operations and mission success. The SLAM community has made great progress in the last decade by enabling real world solutions in terrestrial applications and is nowadays addressing important challenges in robust performance, scalability, high-level understanding, resources awareness and domain adaptation. In this thesis, an adaptive SLAM system is proposed in order to improve rover navigation performance and demand. This research presents a novel localization and mapping solution following a bottom-up approach. It starts with an Attitude and Heading Reference System (AHRS), continues with a 3D odometry dead reckoning solution and builds up to a full graph optimization scheme which uses visual odometry and takes into account rover traction performance, bringing scalability to modern SLAM solutions. A design procedure is presented in order to incorporate inertial sensors into the AHRS. The procedure follows three steps: error characterization, model derivation and filter design. A complete kinematics model of the rover locomotion subsystem is developed in order to improve the wheel odometry solution. Consequently, the parametric model predicts delta poses by solving a system of equations with weighed least squares. In addition, an odometry error model is learned using Gaussian processes (GPs) in order to predict non-systematic errors induced by poor traction of the rover with the terrain. The odometry error model complements the parametric solution by adding an estimation of the error. The gained information serves to adapt the localization and mapping solution to the current navigation demands (domain adaptation). The adaptivity strategy is designed to adjust the visual odometry computational load (active perception) and to influence the optimization back-end by including highly informative keyframes in the graph (adaptive information gain). Following this strategy, the solution is adapted to the navigation demands, providing an adaptive SLAM system driven by the navigation performance and conditions of the interaction with the terrain. The proposed methodology is experimentally verified on a representative planetary rover under realistic field test scenarios. This thesis introduces a modern SLAM system which adapts the estimated pose and map to the predicted error. The system maintains accuracy with fewer nodes, taking the best of both wheel and visual methods in a consistent graph-based smoothing approach

Internal visuomotor models for cognitive simulation processes

Kaiser A. Internal visuomotor models for cognitive simulation processes. Bielefeld: Bielefeld University; 2014.Recent theories in cognitive science step back from the strict separation of perception, cognition, and the generation of behavior. Instead, cognition is viewed as a distributed process that relies on sensory, motor and affective states. In this notion, internal simulations -i.e. the mental reenactment of actions and their corresponding perceptual consequences - replace the application of logical rules on a set of abstract representations. These internal simulations are directly related to the physical body of an agent with its designated senses and motor repertoire. Correspondingly, the environment and the objects that reside therein are not viewed as a collection of symbols with abstract properties, but described in terms of their action possibilities, and thus as reciprocally coupled to the agent. In this thesis we will investigate a hypothetical computational model that enables an agent to infer information about specific objects based on internal sensorimotor simulations. This model will eventually enable the agent to reveal the behavioral meaning of objects. We claim that such a model would be more powerful than classical approaches that rely on the classification of objects based on visual features alone. However, the internal sensorimotor simulation needs to be driven by a number of modules that model certain aspects of the agents senses which is, especially for the visual sense, demanding in many aspects. The main part of this thesis will deal with the learning and modeling of sensorimotor patterns which represents an essential prerequisite for internal simulation. We present an efficient adaptive model for the prediction of optical flow patterns that occur during eye movements: This model enables the agent to transform its current view according to a covert motor command to virtually fixate a given point within its visual field. The model is further simplified based on a geometric analysis of the problem. This geometric model also serves as a solution to the problem of eye control. The resulting controller generates a kinematic motor command that moves the eye to a specific location within the visual field. We will investigate a neurally inspired extension of the eye control scheme that results in a higher accuracy of the controller. We will also address the problem of generating distal stimuli, i.e. views of the agent's gripper that are not present in its current view. The model we describe associates arm postures to pictorial views of the gripper. Finally, the problem of stereoptic depth perception is addressed. Here, we employ visual prediction in combination with an eye controller to generate virtually fixated views of objects in the left and right camera images. These virtually fixated views can be easily matched in order to establish correspondences. Furthermore, the motor information of the virtual fixation movement can be used to infer depth information