Wireless sensor networks for indoor mapping and accurate localization for low speed navigation in smart cities

Abstract : With the increasing demand for urban space, more and more multistory carparks are needed. Although these carparks help to utilize urban space more efficient, they also introduce a new problem. Reports suggest approximately 70 million hours of parking slot searching each year, equivalently 700 million euros loss for France alone. In addition, carparks uses are exceeding their original purposes. Demanding features such as electric charger, online booking of parking spaces, dynamic guidance or mobile payment etc. turn a carpark into a competitive smart environment. One solution to this problem is to develop an autonomous navigation system for intelligent vehicles in the carpark situation. The thesis will identify one of these sub-tasks namely localization in GPS-denied environments. This thesis will present a novel method to solve the indicated problem while keeping the system follows four criteria: availability, scalability, universality and accuracy. There are two main steps: (1) a solution to replicate the GPS behaviour for the GPS-denied environment, and (2) a framework that allows the fusion of GPS-like systems with other localization methods to achieve a high localization accuracy. First, a Wi-Fi Fingerprinting localization system is employed. An approach using an ensemble neural network on a hybrid Wi-Fi fingerprinting database is proposed in this thesis. Experiments in a year-long duration show that this system is capable of localizing vehicles with 2.25m of mean error in the global coordinate frame (WGS84). Second, a complete localization solution must be a fusion of multiple techniques. This allows global as well as local levels of localization to function together. At the same time, having redundancy in the system boosts accuracy and reliability. In this thesis, a flexible fusion framework for multiple localization sensors is proposed. This fusion framework will not only deal with the GPS-denied environment but could be potentially used in the GPS-aided environment and provide a smooth transition between the two areas. To accomplish this demanding task, a Gaussian Mixture Model Particle Filter is developed. While the motion model of this particle filter incorporates data from the IMU (Inertial Measurement Unit) or laser-SLAM, the correction model is a Gaussian mixture model of multiple observations obtained from the Wi-Fi fingerprinting localization system. With two intelligent vehicles (a Cybercar and a Citroen C1 car), 64 experiments were carried out to validate the framework. A mean localization error of 0.5m is achieved in a global coordinate frame. Compare to other solutions with 0.2m of mean localization error in local coordinate frames; this proposed solution has advantages in terms of scalability, availability and universality as well.
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Dinh-Van Nguyen. Wireless sensor networks for indoor mapping and accurate localization for low speed navigation in smart cities. Robotics [cs.RO]. PSL Research University, 2018. English. ⟨NNT : 2018PSLEM029⟩. ⟨tel-02274361⟩

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