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.

Most ultrawideband (UWB) location systems already proposed for position estimation have only been individually evaluated for particular scenarios. For a fair performance comparison among different solutions, a common evaluation scenario would be desirable. In this paper, we compare three commercially available UWB systems (Ubisense, BeSpoon, and DecaWave) under the same experimental conditions, in order to do a critical performance analysis. We include the characterization of the quality of the estimated tag-to-sensor distances in an indoor industrial environment. This testing space includes areas under line-of-sight (LOS) and diverse non-LOS conditions caused by the reflection, propagation, and the diffraction of the UWB radio signals across different obstacles. The study also includes the analysis of the estimated azimuth and elevation angles for the Ubisense system, which is the only one that incorporates this feature using an array antenna at each sensor. Finally, we analyze the 3-D positioning estimation performance of the three UWB systems using a Bayesian filter implemented with a particle filter and a measurement model that takes into account bad range measurements and outliers. A final conclusion is drawn about which system performs better under these industrial conditions.

The estimation of the position of a person in a building is a must for creating Intelligent Spaces. State-of-the-art Local Positioning Systems (LPS) require a complex sensor-network infrastructure to locate with enough accuracy and coverage. Alternatively, Inertial Measuring Units (IMU) can be used to estimate the movement of a person; a methodology that is called Pedestrian Dead-Reckoning (PDR). In this paper, we describe and implement a Kalman-based framework, called INS-EKF-ZUPT (IEZ), to estimate the position and attitude of a person while walking. IEZ makes use of an Extended Kalman filter (EKF), an INS mechanization algorithm, a Zero Velocity Update (ZUPT) methodology, as well as, a stance detection algorithm. As the IEZ methodology is not able to estimate the heading and its drift (non-observable variables), then several methods are used for heading drift reduction: ZARU, HDR and Compass. The main contribution of the paper is the integration of the heading drift reduction algorithms into a Kalman-based IEZ platform, which represents an extended PDR methodology (IEZ+) valid for operation in indoor spaces with local magnetic disturbances. The IEZ+ PDR methodology was tested in several simulated and real indoor scenarios with a low-performance IMU mounted on the foot. The positioning errors were about 1% of the total travelled distance, which are good figures if compared with other works using IMUs of higher performance.

This work presents a robot prototype designed and built for a new aided fruit‐harvesting strategy in highly unstructured environments, involving human‐machine task distribution. The operator drives the robotic harvester and performs the detection of fruits by means of a laser range‐finder, the computer performs the precise location of the fruits, computes adequate picking sequences and controls the motion of all the mechanical components (picking arm and gripper‐cutter). Throughout this work, the specific design of every module of the robotized fruit harvester is presented. The harvester has been built and laboratory tests with artificial trees were conducted to check range‐finder’s localization accuracy and dependence on external conditions, harvesting arm’s velocity, positioning accuracy and repeatability; and gripper‐cutter performance. Results show excellent range‐finder and harvesting arm operation, while a bottleneck is detected in gripper‐cutter performance. Some figures showing overall performance are given.

Several UWB location systems have already been proposed for accurate position estimation. These UWB systems, some available at commercial level, and others implemented as laboratory test-beds, have been individually evaluated for particular applications and under different fusion strategies. In this paper we compare two commercially available UWB systems (Decawave and Bespoon) under exactly the same experimental conditions, in order to generate a critical analysis about their performances. The analysis includes the characterization of the range error in Line-Of-Sight (LOS) and Non-Line-Of-Sight (NLOS) conditions. The NLOS conditions include the propagation/difraction of radio signals across furniture, metallic cabinets and several brick walls in indoor scenarios. The analysis also includes the 2D/3D positioning performance of both UWB systems using a particle filter estimation approach that takes into account NLOS conditions.