Accurate position tracking with a single UWB anchor

Accurate localization and tracking are a fundamental requirement for robotic applications. Localization systems like GPS, optical tracking, simultaneous localization and mapping (SLAM) are used for daily life activities, research, and commercial applications. Ultra-wideband (UWB) technology provides another venue to accurately locate devices both indoors and outdoors. In this paper, we study a localization solution with a single UWB anchor, instead of the traditional multi-anchor setup. Besides the challenge of a single UWB ranging source, the only other sensor we require is a low-cost 9 DoF inertial measurement unit (IMU). Under such a configuration, we propose continuous monitoring of UWB range changes to estimate the robot speed when moving on a line. Combining speed estimation with orientation estimation from the IMU sensor, the system becomes temporally observable. We use an Extended Kalman Filter (EKF) to estimate the pose of a robot. With our solution, we can effectively correct the accumulated error and maintain accurate tracking of a moving robot.Comment: Accepted by ICRA202

UWB-INS Fusion Positioning Based on a Two-Stage Optimization Algorithm

Ultra-wideband (UWB) is a carrier-less communication technology that transmits data using narrow pulses of non-sine waves on the nanosecond scale. The UWB positioning system uses the multi-lateral positioning algorithm to accurately locate the target, and the positioning accuracy is seriously affected by the non-line-of-sight (NLOS) error. The existing non-line-of-sight error compensation methods lack multidimensional consideration. To combine the advantages of various methods, a two-stage UWB-INS fusion localization algorithm is proposed. In the first stage, an NLOS signal filter is designed based on support vector machines (SVM). In the second stage, the results of UWB and Inertial Navigation System (INS) are fused based on Kalman filter algorithm. The two-stage fusion localization algorithm achieves a great improvement on positioning system, it can improve the localization accuracy by 79.8% in the NLOS environment and by 36% in the (line-of-sight) LOS environment

WIFI BASED INDOOR POSITIONING - A MACHINE LEARNING APPROACH

Navigation has become much easier these days mainly due to advancement in satellite technology. The current navigation systems provide better positioning accuracy but are limited to outdoors. When it comes to the indoor spaces such as airports, shopping malls, hospitals or office buildings, to name a few, it will be challenging to get good positioning accuracy with satellite signals due to thick walls and roofs as obstacles. This gap led to a whole new area of research in the field of indoor positioning. Many researches have been conducting experiments on different technologies and successful outcomes have beenseen. Each technology providing indoor positioning capability has its own limitations. In this thesis, different radio frequency (RF) and non-radio frequency (Non-RF) technologies are discussed but focus is set on Wi-Fi for indoor positioning. A demo indoor positioning app is developed for the Technobothnia building at the University of Vaasa premises. This building is already equipped with Wi-Fi infrastructure. A floor plan of the building, radio maps and a fingerprinting database with Wi-Fi signal strength measurements is created with help of tools from HERE technology. The app provides real-time positioning and routing as a future visitor tool. With the exceeding amounts of available data, one of the highly popular fields is applying Machine Learning (ML) to data. It can be applied in many disciplines from medicine to space. In ML, algorithms learn from the data and make predictions. Due to the significant growth in various sensor technologies and computational power, large amounts of data can be stored and processed. Here, the ML approach is also taken to the indoor positioning challenge. An open-source Wi-Fi fingerprinting dataset is obtained from Tampere University and ML algorithms are applied on it for performing indoor positioning. Algorithms are trained with received signal strength (RSS) values with their respective reference coordinates and the user location can be predicted. The thesis provides a performance analysis of different algorithms suitable for future mobile implementations

Decentralized Collaborative Localization Using Ultra-Wideband Ranging

This thesis summarizes the development of a collaborative localization algorithm simulation environment and the implementation of collaborative localization using Ultra-Wideband ranging in autonomous vehicles. In the developed simulation environment, multi-vehicle scenarios are testable with various sensor combinations and configurations. The simulation emulates the networking required for collaborative localization and serves as a platform for evaluating algorithm performance using Monte Carlo analysis. Monte-Carlo simulations were run using a number of situations and vehicles to test the efficacy of UWB sensors in decentralized collaborative localization as well as landmark measurements within an extended Kalman filter. Improvements from adding Ultra-Wideband ranging were shown in all simulated environments, with landmarks offering additional improvements to collaborative localization, and with the most significant accuracy improvements seen in GNSS-denied environments. Physical experiments were run using a by-wire GEM e6 from Autonomous Stuff in an urban environment in both collaborative and landmark setups. Due to higher than expected INS certainty, adding UWB measurements showed smaller improvements than simulations. Improvements of 9.2 to 12.1% were shown through the introduction of Ultra-Wideband ranging measurements in a decentralized collaborative localization algorithm. Improvements of 30.6 to 83.3% were shown in using UWB ranging measurements to landmarks in an Extended Kalman Filter for street crossing and tunnel environments respectively. These results are similar to the simulated data, and are promising in showing the efficacy of adding UWB ranging sensors to cars for collaborative and landmark localization, especially in GNSS-denied environments. In the future, additional moving vehicles with additional tags will be tested and further evaluations of the UWB ranging modules will be performed

UWB sensor based indoor LOS/NLOS localization with support vector machine learning

Ultra-wideband (UWB) sensor technology is known to achieve high-precision indoor localization accuracy in line-of-sight (LOS) environments, but its localization accuracy and stability suffer detrimentally in non-line-of-sight (NLOS) conditions. Current NLOS/LOS identification based on channel impulse response’s (CIR) characteristic parameters (CCP) improves location accuracy, but most CIR-based identification approaches did not sufficiently exploit the CIR information and are environment specific. This paper derives three new CCPs and proposes a novel two-step identification/classification methodology with dynamic threshold comparison (DTC) and the fuzzy credibility-based support vector machine (FC-SVM). The proposed SVM based classification methodology leverages on the derived CCPs obtained from the waveform and its channel analysis, which are more robust to environment and obstacles dynamic. This is achieved in two-step with a coarse-grained NLOS/LOS identification with the DTC strategy followed by FC-SVM to give the fine-grained result. Finally, based on the obtained identification results, a real-time ranging error mitigation strategy is then designed to improve the ranging and localization accuracy. Extensive experimental campaigns are conducted in different LOS/NLOS scenarios to evaluate the proposed methodology. The results show that the mean LOS/NLOS identification accuracy in various testing scenarios is 93.27 %, and the LOS and NLOS recalls are 94.27 % and 92.57 %, respectively. The ranging errors in LOS(NLOS) conditions are reduced from 0.106 m(1.442 m) to 0.065 m(0.739 m), demonstrating an improvement of 38.85 %(48.74 %) with 0.041 m(0.703 m) error reduction. On the other hand, the average positioning accuracy is also reduced from 0.250 m to 0.091 m with an improvement of 63.49 %(0.159 m), which outperforms the state-of-the-art approaches of the Least-squares support vector machine (LS-SVM) and K-Nearest Neighbor (KNN) algorithms

UWB ranging errors mitigation with novel CIR feature parameters and two-step NLOS identification

The effective identification and mitigation of non-line-of-sight (NLOS) ranging errors are essential for achieving high-precision positioning and navigation with ultra-wideband (UWB) technology in harsh indoor environments. In this paper, an efficient UWB ranging-error mitigation strategy that uses novel channel impulse response parameters based on the results of a two-step NLOS identification, composed of a decision tree and feedforward neural network, is proposed to realize indoor locations. NLOS ranging errors are classified into three types, and corresponding mitigation strategies and recall mechanisms are developed, which are also extended to partial line-of-sight (LOS) errors. Extensive experiments involving three obstacles (humans, walls, and glass) and two sites show an average NLOS identification accuracy of 95.05%, with LOS/NLOS recall rates of 95.72%/94.15%. The mitigated LOS errors are reduced by 50.4%, while the average improvement in the accuracy of the three types of NLOS ranging errors is 61.8%, reaching up to 76.84%. Overall, this method achieves a reduction in LOS and NLOS ranging errors of 25.19% and 69.85%, respectively, resulting in a 54.46% enhancement in positioning accuracy. This performance surpasses that of state-of-the-art techniques, such as the convolutional neural network (CNN), long short-term memory–extended Kalman filter (LSTM-EKF), least-squares–support vector machine (LS-SVM), and k-nearest neighbor (K-NN) algorithms

Évaluation du potentiel d'une technologie de positionnement intérieur par trilatération ultrasonore pour l'amélioration des trajectoires d'un système lidar mobile

Depuis quelques années, les techniques de positionnement intérieur deviennent de plus en plus importantes et trouvent leur place dans divers domaines, dont l'arpentage souterrain. Le présent travail a été réalisé dans le cadre du projet MinEyes, dont l'un des principaux objectifs est d'améliorer le positionnement d'un système lidar mobile (SLM) à l'intérieur des tunnels miniers souterrains. Cela permettra d'effectuer une cartographie 3D précise et rapide de ces environnements. Cependant, l'absence de signal GNSS rend cette tâche difficile. L'une des solutions mise en place pour résoudre ce problème est l'utilisation d'une centrale inertielle. Cependant, l'un des inconvénients majeurs de cette technique est l'accumulation d'erreurs dans le temps ce qui provoque la dérive de la trajectoire. Nous proposons donc une méthode combinant un système de positionnement intérieur basé sur la trilatération ultrasonore et un SLM. Nous utilisons pour cela un ensemble de balises ultrasons fixes dont les positions sont connues et qui communiquent avec une cible ultrason mobile lui permettant d'estimer sa trajectoire. La qualité de la trajectoire ainsi estimée est évaluée à l'aide de données obtenues avec une station totale robotisée. Nos expériences ont démontré une incertitude constante de la trajectoire produite sur toute sa longueur. Les écarts obtenus sont de 2 cm pour la coordonnée X (axe suivant la largeur du corridor) et 4 cm pour la coordonnée Y (axe de déplacement de la cible mobile). Suite à la validation de la qualité de la trajectoire générée, nous avons procédé à l'intégration de la cible ultrason mobile sur un SLM servant à la localisation et la production de nuages de points 3D sur un robot conçu sur la plateforme Raspberry Pi, exploitant la librairie ROS (Robot Operating System) et permettant la production de nuages de points géoréférencés. La comparaison du nuage de points ainsi produit avec un nuage de points de référence produit avec un scanneur LiDAR 3D (Faro X130) a montré un écart uniforme ne dépassant pas 4 mm pour 67% de la zone couverte. Ce travail démontre donc le potentiel du système de navigation proposé, basé sur la technologie de trilatération ultrasonore, qui permet d'éviter la dérive d'une plateforme mobile en milieu intérieure.In recent years, indoor positioning techniques have become increasingly important and are finding their way into various fields, including underground surveying. The present work was carried out within the framework of the MinEyes project. One of its main goals is to improve the positioning of a mobile lidar system (MLS) inside underground mining tunnels. This will allow accurate and fast 3D mapping of these environments. However, the lack of a GNSS signal makes this task difficult. One of the solutions implemented to solve this problem is the use of an inertial measurement unit. However, one of the major drawbacks of this technique is the accumulation of errors over time, which causes the trajectory to drift. We therefore propose a method combining an indoor positioning system based on ultrasonic trilateration and an MLS. We use a set of fixed ultrasonic beacons whose positions are known, and which communicate with a mobile ultrasonic target allowing it to estimate its trajectory. The quality of the estimated trajectory is evaluated using data obtained simultaneously with a robotic total station. Our experiments demonstrated a constant uncertainty of the produced trajectory over its entire length. This uncertainty reaches 2 cm for the X coordinate (axis following the width of the corridor) and 4 cm for the Y coordinate (axis of movement of the mobile target). Following the validation of the quality of the generated trajectory, we proceeded to the integration of the mobile ultrasound target on an MLS used for the localization and the production of 3D point clouds of a robot designed on the Raspberry Pi platform, exploiting the ROS library (Robot Operating System) and allowing the production of georeferenced point clouds. The comparison of a point cloud produced in this way with a reference point cloud produced with a 3D LiDAR scanner (Faro X130) showed a uniform deviation not exceeding 4 mm for 67% of the area covered. This work demonstrates the potential of the proposed navigation system, based on the technology of ultrasonic trilateration, which allows avoiding the drift of a mobile platform in indoor environment