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Accurate Pedestrian Indoor Navigation by Tightly Coupling Foot-Mounted IMU and RFID Measurements

We present a new method to accurately locate persons indoors by fusing inertial navigation system (INS) techniques with active RFID technology. A foot-mounted inertial measuring units (IMUs)-based position estimation method, is aided by the received signal strengths (RSSs) obtained from several active RFID tags placed at known locations in a building. In contrast to other authors that integrate IMUs and RSS with a loose Kalman filter (KF)-based coupling (by using the residuals of inertial- and RSS-calculated positions), we present a tight KF-based INS/RFID integration, using the residuals between the INS-predicted reader-to-tag ranges and the ranges derived from a generic RSS path-loss model. Our approach also includes other drift reduction methods such as zero velocity updates (ZUPTs) at foot stance detections, zero angular-rate updates (ZARUs) when the user is motionless, and heading corrections using magnetometers. A complementary extended Kalman filter (EKF), throughout its 15-element error state vector, compensates the position, velocity and attitude errors of the INS solution, as well as IMU biases. This methodology is valid for any kind of motion (forward, lateral or backward walk, at different speeds), and does not require an offline calibration for the user gait. The integrated INS+RFID methodology eliminates the typical drift of IMU-alone solutions (approximately 1% of the total traveled distance), resulting in typical positioning errors along the walking path (no matter its length) of approximately 1.5 m.

Pedestrian indoor navigation by aiding a foot-mounted IMU with RFID Signal Strength measurements

We present a methodology to accurately locate persons indoors by fusing Inertial Navigation (INS) techniques with active RFID technology. A foot-mounted IMU aided by the Received Signal Strengths (RSS) obtained from several active RFID tags, placed at known locations in a building, has been used. Other authors have already integrated IMUs with RFID tags in loosely-coupled Kalman Filter (KF) solutions [1], [2], [3]. They feed the KF with the residuals of inertial- and RFID-calculated positions; these approaches do not exploit the benefits of Zero Velocity Updates (ZUPT). In this paper, we present a tight KF-based INS/RFID integration using the residual between the INS-predicted range-to-tag, and the range derived from a generic RSS path-loss model. Our approach also includes ZUPTs at detected foot stances, ZARU (Zero Angular-rate Update) estimation at still phases, and heading drift reduction using magnetometers. A 15-element error state Extended KF [4], [7] compensates position, velocity and attitude errors of the INS solution, as well as IMU biases. This methodology is valid for any kind of motion (forward, lateral or backwards walk, at different speeds) and does not require an specific off-line calibration, neither for the user gait, nor for the location-dependent RSS fading in the building. The integrated INS+RFID methodology eliminates the typical drift of IMU-alone solutions (approximately 1% of the total travelled distance), accounting for typical positioning errors along the walking path (no matter its length) of approximately 1.5 meters.

Indoor Positioning Using Efficient Map Matching, RSS Measurements, and an Improved Motion Model

Unlike outdoor positioning, there is no unique solution to obtain the position of a person inside a building or in Global Navigation Satellite System (GNSS)-denied areas. Typical implementations indoor rely on dead reckoning or beacon-based positioning, but a robust estimation must combine several techniques to overcome their own drawbacks. In this paper, we present an indoor positioning system based on foot-mounted pedestrian dead reckoning (PDR) with an efficient map matching, received signal strength (RSS) measurements, and an improved motion model that includes the estimation of the turn rate bias. The system was implemented using a two-level structure with a low-level PDR filter and a high-level particle filter (PF) to include all the measurements. After studying the effect of the step displacement on the PFs proposed in the literature, we concluded that a new state with the turn rate bias (a nonobservable state in PDR) is needed to correctly estimate the error growth and, in the long term, correct the position and heading estimation. Additionally, the wall crossing detection of map matching was optimized as matrix operations, and a room grouping algorithm was proposed as a way to accelerate the process, achieving real-time execution with more than 100 000 particles in a building with more than 600 wall segments. We also include a basic path-loss model to use RSS measurements that allows a better initialization of the filter, fewer particles, and faster convergence, without the need for an extensive calibration. The inclusion of the map matching algorithm lowers the error level of the RSS-PDR positioning, from 1.9 to 0.75 m, 90% of the time. The system is tested in several trajectories to show the improvement in the estimated positioning, the time to convergence, and the required number of particlesThis work was supported by the LEMUR project (TIN2009-14114-C04-02), LORIS project (TIN2012-38080-C04-04),SMARTLOC project (CSIC-PIE Ref.201450E011) and the JAE PREDoc program. European Commission y Consejo Superior de Investigaciones Cientificas (Espana)

Improving indoor positioning using an efficient Map Matching and an extended motion model

Unlike outdoor positioning, there is no unique solution to obtain the position of a person inside a building or in Global Navigation Satellite System (GNSS)-denied areas. Typical implementations indoor rely on dead reckoning or beacon-based positioning, but a robust estimation must combine several techniques to overcome their own drawbacks. In this paper, we present an indoor positioning system based on foot-mounted pedestrian dead reckoning (PDR) with an efficient map matching, received signal strength (RSS) measurements, and an improved motion model that includes the estimation of the turn rate bias. The system was implemented using a two-level structure with a low-level PDR filter and a high-level particle filter (PF) to include all the measurements. After studying the effect of the step displacement on the PFs proposed in the literature, we concluded that a new state with the turn rate bias (a nonobservable state in PDR) is needed to correctly estimate the error growth and, in the long term, correct the position and heading estimation. Additionally, the wall crossing detection of map matching was optimized as matrix operations, and a room grouping algorithm was proposed as a way to accelerate the process, achieving real-time execution with more than 100 000 particles in a building with more than 600 wall segments. We also include a basic path-loss model to use RSS measurements that allows a better initialization of the filter, fewer particles, and faster convergence, without the need for an extensive calibration. The inclusion of the map matching algorithm lowers the error level of the RSS-PDR positioning, from 1.9 to 0.75 m, 90% of the time. The system is tested in several trajectories to show the improvement in the estimated positioning, the time to convergence, and the required number of particlesThis work was supported by the LEMUR project (TIN2009-14114-C04-02), LORIS project (TIN2012-38080-C04-04),SMARTLOC project (CSIC-PIE Ref.201450E011) and the JAE PREDoc program. European Commission y Consejo Superior de Investigaciones Cientificas (Espana)

A Short-Range Ship Navigation System Based on Ladar Imaging and Target Tracking for Improved Safety and Efficiency

A new maritime navigation system based on a laser range-finder scanner for obstacle avoidance and precise maneuvering operations is described in this paper. The main novelty of this work is the adaptation and implementation of known technology for laser range finding and algorithms for target tracking into a system that operates in real time and has been tested in different natural sea and inland navigation scenarios. The principal components of this system, namely, 1) the laser range finder, 2) the scanning unit, and 3) the data processing and displaying unit, are described in detail. Ladar images are dense horizontally and sparse vertically as a compromise between capturing relevant features and quick frame formation. Images are processed for range outlier removal, and significant observable patterns are extracted. This multiple-target tracking problem is tackled using robust Kalman filtering techniques for continuous tracking of each detected observation. We minimize unreliable track initializations and preserve tracks from deletion during temporal misobservations. The evaluation in open-sea and inland waterways gave good results, making the system valid for precise maneuvering, fluent navigation, and accident mitigation. Objects of interest, from boats to ships, are detected and robustly tracked; pier and lock chamber sketches are reliable; bridge height estimation is precise; and narrow waterways (river banks and bridge columns) are correctly detected. The prototype developed can be considered to be a very valuable complementary device to traditional radar-based techniques that are not totally valid for accurate short-range exploration, improving efficiency and safety in ship operations.

A Real-Time Tool Positioning Sensor for Machine-Tools

In machining, natural oscillations, and elastic, gravitational or temperature deformations, are still a problem to guarantee the quality of fabricated parts. In this paper we present an optical measurement system designed to track and localize in 3D a reference retro-reflector close to the machine-tools drill. The complete system and its components are described in detail. Several tests, some static (including impacts and rotations) and others dynamic (by executing linear and circular trajectories), were performed on two different machine tools. It has been integrated, for the first time, a laser tracking system into the position control loop of a machine-tool. Results indicate that oscillations and deformations close to the tool can be estimated with micrometric resolution and a bandwidth from 0 to more than 100 Hz. Therefore this sensor opens the possibility for on-line compensation of oscillations and deformations.

Computer vision system for estimating and controlling the weight of glass gobs during their industrial formation process

We present a computer vision system for measuring the weight of gobs during a glass-forming process, and a control strat- egy to automatically correct any weight deviation from a given set point. The system is based on a reliable gob area estimation using image-processing algorithms. A monochrome CCD high-resolution camera and a photodetector for synchronizing acquisition are used for registering gob images. Assuming that the gob has symmetry of revolution about the vertical axis, the proposed system estimates the weight of gobs with accuracy better than 60.75%. A learning weight control strategy is proposed based on a proportional-integral (PI)-repetitive control scheme. The weight deviation from a set point is used as a control signal to adjust the glass flow into the feeder. This regulation scheme enables effective weight control, canceling mid- and long-term effects. The tracking error of 61.5% means a reduction of 40% when compared with a traditional PI controller.