Accurate Indoor Positioning Based on Learned Absolute and Relative Models

It can be considered as a current advancement observed within the automotive industry that vehicles are more and more tightly interconnected. By utilizing the data provided by the manifold onboard sensors the vehicles can exchange considerable amounts of perceived environmental data with each other or a common fusion center. This way comprehensive, richly detailed, and by now unmatched up-to-date maps can be deduced by aggregating the received data. Subsequently, these maps can be utilized as an additional, virtual, ultra-longrange sensor for supporting next generation driver assistance or piloted driving functions. The focus of the research on this topic has usually concerned the key challenge, data aggregation; this means how to fuse multiple sensor readings afflicted with imperfections and uncertainties. However, to achieve generalized, reliable, precise, and computationally feasible aggregates the full data acquisition and processing chain needs to be considered holistically as affirmed by our research. At this, highly interesting, however, usually neglected challenges arise which this paper is dedicated to. We illuminate the for a real-world automotive application crucial aspects of sensor data fusion, such as coping with the temporal decay of measurements, the precise vehicle localization by utilizing commercially viable sensors, the generalized storage of different types of sensor data, and the definition of generalized acquisition and processing chains to provide a fast adaptation to new kinds of input data. Furthermore, we present how these aspects lead to an adaptable, efficient, and accurate data fusion of collective vehicle data concerning both areal and point-shaped/complex data.

The mobile robot which accomplishes a work in explored region does not know location information of surroundings. Traditionally, simultaneous localization and mapping(SLAM) algorithms solve the localization and mapping problem in explored regions. Among the several SLAM algorithms, the EKF (Extended Kalman Filter) based SLAM is the scheme most widely used. The EKF is the optimal sensor fusion method which has been used for a long time. The odometric error caused by an encoder can be compensated by an EKF, which fuses different types of sensor data with weights proportional to the uncertainty of each sensor. In many cases the EKF based SLAM requires artificially installed features, which causes difficulty in actual implementation. Moreover, the computational complexity involved in an EKF increases as the number of features increases. And SLAM is a weak point of long operation time. Therefore, this paper presents a symmetrical model based SLAM algorithm (called M-SLAM).

The U.S. Army has a critical need for the capability provided by a multifunction sensor. This is (in effect) a smart sensor system that can adapt to environmental conditions and adjust its mode of operation to effectively counter any threat it meets. It will have an intelligent signal processor which has all of the system's sensor signals to choose from. The processor chooses the appropriate signal information to rapidly detect, acquire, track, and automatically identify all targets in the vicinity of the sensor under a wide variety of battlefield scenarios and environmental conditions. The multiphenomenology signal information provides the flexibility to overcome the adverse effects of clutter, countermeasures (both active and passive), illumination, obscurants, target orientation, and weather. It should be noted, however, that the types of sensory information required is dependent on the mission and the operating environment. For instance, a strategic defense sensor operating in space can use (and will need) different types of sensor data than the multifunction sensor employed on an attack helicopter. In fact, the sensor configuration on a helicopter operating in Saudi Arabia may be quite different from one that is deployed to Vietnam. For the purpose of this paper we generalize about the technologies desired for an adaptable, `smart' sensor system. We do not specify a particular mission nor define a specific threat. However, in any case, we can assume the need to fuse sensor signal information in an intelligent processor to provide robust performance in the battlefield environment. 12© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Early detection of age-related disease symptoms in older people by the use of daily activity data is one of the central challenges of home sensor systems. This paper focuses on dementia scale classification from daily activity data collected using sensors that can be deployed in actual residential environments. Activity data collected by four sensors (a door sensor, human motion sensor, location sensor, and sleep sensor) were obtained by recording 56 older adults living in common residences. We analyzed the effects of different types of sensor data, such as time spent in an individual room according to human motion sensors, location in a facility, and sleep patterns, on dementia detection. We then developed a feature extraction method related to daily activity patterns based on a clustering algorithm and analyzed its effectiveness. In the experimental evaluation, we trained binary classification models to classify dementia scale scores based on the Mini-Mental State Examination (MMSE) from these datasets. The experimental results show that a maximum accuracy of 0.871 was obtained with a linear support vector machine (SVM) model by fusing the door, location, and sleep features and by clustering activity patterns using the X-means algorithm.

Hypertensive disorders present significant morbidity and mortality during pregnancy. Although ambulatory blood pressure measurement remains the standard of care for normotensive women, self-monitoring at home is increasingly prevalent. The widespread use of smartphones worldwide has sparked interest in mobile applications that leverage the built-in hardware for blood pressure estimation, yet few trials have assessed their accuracy. This prospective, longitudinal and monocentric study evaluated the accuracy of the OptiBP algorithm against standard oscillometric blood pressure measurements in a sample of pregnant women. Patients scheduled for elective caesarean sections were enrolled during the preoperative anesthesia consultations. Paired blood pressure measurements using OptiBP and the reference method were obtained at multiple time-points in late pregnancy and the postpartum period. Agreement between methods was assessed using the AAMI/ESH/ISO 81060-2:2018 standard thresholds of 5 ± 8 mmHg for mean ± standard deviation of the error (criterion 1) and patient-specific standard deviation of the mean error (criterion 2) and represented graphically by Bland-Altman scatterplots. Forty-eight women were enrolled of which 32 completed the protocol, yielding 338 total valid measurement pairs. Mean and standard deviation of the error were -1.78 ± 7.94 and 1.19 ± 7.59, and the patient-specific standard deviation of the mean error was 4.68 and 4.52, for SBP and DBP, respectively. Compared with blood pressure measurements taken with an oscillometric device, OptiBP's blood pressure estimates meet the AAMI/ESH/ISO 81060-2:2018 criteria.

The traditional global template matching is time consuming, has low accuracy, and cannot be adapted to rotation and scale change. The template matching technique proposed in this study improves the time, accuracy and robustness for printed circuit boards (PCB). In order to shorten the image positioning time, the image preprocessing is implemented on PCB image and the image blocks are labeled to obtain the tagged image, and the feature vector is extracted and the marking point region image is selected. The feature vector with rotation change and scale change robustness is extracted from the tagged image after labeling in the PCB image by using artificial neural network, combined with image moments for training. The marking point region image in the PCB image is selected. The scale value of the marking point region image is estimated by parametric template vector matching. The deflection angle of marking point region image is calculated by Hough transform. The obtained scale value and deflection angle value are used for fast template matching to determine the marking point positioning. The three-dimensional (3D) parabolic curve fitting is implemented in marking point positioning and adjacent pixel position to reach the sub-pixel level accuracy. The experiment showed that the proposed template matching technique for the PCB image with or without noise or angle rotation, the average position accuracy error of each translated image is lower than 7 \(\upmu \)m, and the error standard deviation is lower than 5 \(\upmu \)m. The rotation angle error average and standard deviation of angular error of Hough transform are lower than 0.2\(^{\circ }\), more accurate than orientation code (OC) method. The scale value estimation, relative error average and error standard deviation are lower than 0.004 and 0.006 for the image with or without noise. The average complete positioning time of PCB image at resolution of \(2500\times 2500\) is only 0.55 s, which is better than the 3.97 s of traditional global template matching. The results prove that the template matching technique of this study not only has sub-pixel level high accuracy and short computing time, but also has the robustness of rotation change and scale change interference. It can implement rapid, efficient and accurate positioning.

Small Unmanned Aerial Systems (sUAS) operations are increasing in demand and complexity. Using multiple cooperative sUAS (i.e., a swarm) can be beneficial and is sometimes necessary to perform certain tasks (e.g. precision agriculture, mapping, surveillance) either independent or collaboratively. However, controlling the flight of multiple sUAS autonomously and in real-time in a challenging environment in terms of obstacles and navigation requires highly accurate absolute and relative position and velocity information for all platforms in the swarm. This information is also necessary to effectively and efficiently resolve possible collision encounters between the sUAS. In our swarm, each platform is equipped with a Global Navigation Satellite System (GNSS) sensor, an inertial measurement unit (IMU), a baro-altimeter and a relative range sensor (range radio). When GNSS is available, its measurements are tightly integrated with IMU and baro-altimeter measurements to obtain the platform's absolute position. At the same time, the raw measurements are exchanged with the other platforms to obtain a highly accurate relative position and velocity solution with integrity. In the presence of GNSS, this relative position and velocity is used to calibrate the range radios. When GNSS is not available due to external factors (e.g., obstructions, interference), the position and velocity estimators switch to an integrated solution based on IMU, baro and relative range measurements, to maintain an accurate relative position estimate, and reduce the drift in the swarm's absolute position estimate as is typical of an IMU-based system. Multiple multi-copter data collection platforms have been developed and equipped with GNSS, inertial sensors and range radios, which were developed at Ohio University. This paper outlines the underlying methodology, the platform hardware components and analyzes and discusses sUAS flight data results.

Modern navigation solutions rely on a combination of inertial measurement units (IMUs) and a global navigation satellite system (GNSS) receiver to estimate a navigating body's position. In order to produce a high-fidelity solution, the current approach is to utilize a single ultra-low bias navigational grade IMU in the system. However, these high-quality IMUs are expensive, bulky, heavy, and require significant power consumption. This article proposes the fusion of multiple lower-quality IMUs to achieve near-identical or better positional accuracy as a single high-quality sensor to minimize cost, size, weight, and power (C-SWaP) without sacrificing the positional estimation accuracy. The primary focus is on a generalized method to fuse multiple position estimations from multiple co-located IMUs for a single navigating body. The proposed fusion algorithm is applied to simulated data produced by three precision micro-electromechanical systems (MEMS) grade IMU modules (Analog Devices ADIS16465) from two different simulated flight paths. The absolute error is calculated between the position estimation generated by the proposed algorithm and the “truth” position provided by the simulation to determine the accuracy of the final result. The error of the proposed algorithm using the Analog Devices modules is then compared to the error between a single navigational grade IMU (NovAtel IMU-ISA-100 C) and the simulated “truth” position. The results show that using only three precision MEMS grade IMUs; the proposed method can produce identically accurate position estimations as a navigational grade IMU while drastically reducing C-SWaP. This result is further validated in measured data from an instrumented test setup using the above mentioned IMU configurations fused with a standard GPS and compared to a real-time kinematic GNSS setup used as a the third-party ground truth. In addition, the proposed method is further validated by integrating the two navigation systems with a Ku-band radar to produce synthetic aperture radar images. The image produced using the multi-IMU configuration as opposed to the navigation-grade IMU is more focused. Given these results, the proposed method has been validated in theory, simulation, and measurement, resulting in an order of magnitude reduction in cost, size, and power consumption, as well as a three-and-a-half times weight reduction of the overall IMU solution.

Detecting individuals' spatial familiarity with urban environments using eye movement data

Personal location discovery and navigation within buildings has become an important research topic in the last years. One method to determine one's current position based on mobile-devices is to compare the set of available WiFi access points (APs), i.e. the fingerprint of a given space, to a previously collected database. In this context, this paper addresses the inherent problem of such systems that this fingerprint database needs to be established beforehand. Thus, situations can occur where a building is only partially represented in the database and localization can only be provided in a subset of the spaces of the building. This problem occurs especially in crowd-sourcing (organic) approaches where users consecutively contribute location-binds. In these situations an additional system is needed to provide localization. We present a first study on the fusion of pedestrian dead reckoning (PDR) from inertial sensors with position estimates from a WiFi localization system. We outline a possible design of particle filter and analyze its behavior on experimental data. We conclude that the outlined method can help to improve WiFi localization and is especially useful within crowd-sourcing environments.

The NewSpace landscape for microlaunchers is highly competitive. The cost is one of the main drivers in this competition. Several microlauncher companies have decided to outsource the GNC sub-system to external providers, procuring low-cost commercial-of-the-shelf solutions. Lately, Astos has been involved in several activities related with GNC for (micro-)launchers. Astos’ work has been focused on providing GNC development tools and flexible and easily reconfigurable GNC software that can be applied to a wide range of launch vehicles. Navigation is one of the main drivers for launcher orbital injection accuracy and it is also one of the main drivers for cost in the GNC sub-system. Usually, this cost is driven by the Inertial Navigation System (INS) or Inertial Measurement Unit (IMU), in which expensive technologies are employed such as ring-laser gyroscopes. In traditional inertial propagation algorithms, errors are accumulated due to integration resulting in a steady divergence of the position and attitude estimate. Therefore, the performance of the navigation mainly depends on the performance of the IMU. This makes an investment into a highly accurate inertial sensors, especially the gyroscope, necessary to meet the navigation requirements. Lately, the use of hybrid navigation solutions that fuse the inertial measurements with Global Navigation Satellite Systems (GNSS) measurements has diminished, in some degree, the reliance of the navigation algorithm on the IMU performance. Although this is true for translational states, as position and velocity, the same cannot be said with the same degree of validity for attitude determination and, more specifically, for roll angle estimation. One possible way to reduce the cost of inertial sensors is by using microelectromechanical systems (MEMS). The advantages of using this technology, apart from the large reduction in cost, are the significant saves in mass and power required to operate these sensors. The disadvantage is clearly the lower performance, especially in high-vibration environments such as encountered in launcher vehicles. Lately, efforts to improve the performance of MEMS navigation solutions were driven by the demand of industries such as the wearables and unmanned aerial systems. This led to the development of solutions that utilize multiple low-cost inertial measurement units. By that, the hardware cost is not gravely increased in exchange for higher navigation accuracy, reliability, and redundancy. In this paper, a navigation algorithm is proposed that incorporates the measurements from multiple low-grade MEMS IMUs. The algorithm implements parallel Extended Kalman Filters (EKF), one for each IMU, combining the inertial measurements with the position, velocity, and time solutions provided by a single GNSS receiver. The algorithm has a loosely coupled architecture and the inertial propagation is regularly reset using the fused estimates of the vehicle’s state and IMU biases (closed-loop implementation). The navigation solution, providing estimates of position, velocity, and attitude, is then a combination of the individual EKF results through weighted means based on the covariance of the individual estimates. This formulation utilizing parallel filters is called in the literature as Federated Kalman Filter architecture. An analysis will be presented relating the performance of the navigation architecture with the number of sensors used and their distribution on the launch vehicle’s body. The assessment is performed with relation to a baseline navigation architecture also implementing a loosely coupled, closed-loop, hybrid structure but employing a single low-grade IMU. The analysis will determine the most promising sensor constellation, based on navigation performance, cost and implementation complexity. The selected multiple IMU solution is then compared with two different navigation architectures: Strapdown inertial propagation utilizing a single high-grade IMU; and hybrid navigation scheme implementing a loosely coupled, closed-loop architecture with a medium grade IMU. The performance evaluation is conducted utilizing simulated measurements retrieved from high-fidelity 6 degrees-of-freedom non-linear simulator of a representative microlauncher scenario. The simulators’ dynamics, kinematics, environment, and sensor modelling are provided by the ASTOS tool. Astos intends to further study the feasibility and performance of the proposed Inertial/GNSS hybrid navigation architecture with multiple IMUs. In the roadmap for raising the algorithm’s technology readiness level, more advanced simulations are foreseen, with improved sensor error modelling and previously unmodelled dynamical effects such as flexibility and sloshing. Additionally, processor- and hardware-in-the-loop tests with real sensor data are planned.

Abstract. With the continuous improvement of the hardware level of the inertial measurement unit (IMU), indoor pedestrian dead reckoning (PDR) using an inertial device has been paid more and more attention. Typical PDR system position estimation is based on acceleration obtained from accelerometers to measure the step count, estimate step length and generate the position with the heading received from angular sensors (magnetometers and gyroscopes). Unfortunately, collected signals are very responsive to the alignment of sensor devices, built-in instrumental errors and distortions from the surrounding environment. In our work, a pedestrian positioning method using step detection based on a shoe-mounted inertial unit is arranged and put to the test, and the final results are analyzed. The extended Kalman filter (EKF) provides estimation of the errors which are acquired by the XSENS IMU sensor biases. The EKF is revised with acceleration and angular rate computations by the ZUPT (zero velocity update) and ZARU (zero angular rate update) algorithms. The step detection associated with these two solutions is the perfect choice to calculate the current position and distance walked and to estimate the IMU sensors' collected errors by using EKF. The test with a shoe-mounted IMU device was performed and analyzed in order to check the performance of the recommended method. The combined PDR final results were compared to GPS/Beidou postprocessing kinematic results (outdoor environment) and to a real route which was prepared and calculated for an indoor environment. After the comparison, the results show that the accuracy of the regular-speed walking under ZUPT and ZARU combination in the case of outdoor positioning did not go beyond 0.19 m (SD) and for indoor positioning accuracy did not exceed 0.22 m (SD). The authors are conscious that built-in drift errors coming from accelerometers and gyroscopes, as well as the final position obtained by XSENS IMU, are only stable for a short time period. Based on this consideration, our future work will be focused on supporting the methods presented with radio technologies (WiFi) or image-based solutions to correct all IMU imperfections.

Augmented reality (AR) technologies enhance users knowledge of their immediate surroundings by presenting contextualized and spatially relevant information. This augmentation is enabled by the automatic estimation of the device's pose with respect to the environment. Many simple AR applications for smartphones are well served by the pose estimate provided by GPS and inertial sensors, while other demand an higher accuracy which can be obtained with vision-based methods. The application presented in this paper, which aims to augment pictures of mountainous landscapes with geo-referenced data, belongs to the latter category. Our application is based on a novel approach for image-to-world registration which jointly relies on inertial and visual sensors. In a nutshell, first GPS and inertial sensors are used to compute a rough estimate of the device position and pose, then visual data are employed to refine it. Specifically, a learning-based edge detection algorithm is used to extract mountain profiles from the picture of interest. Then, a novel registration algorithm based on a robust optimization framework is employed to align the computed profiles to synthetic ones obtained from Digital Elevation Models. Our experiments, conducted on a novel dataset of manually aligned pictures which is made publicly available, demonstrate that the proposed registration method guarantees an increased accuracy with respect to competing methods and it is computationally efficient when implemented on a smartphone.

This paper presents a new method for 3D position and orientation estimation based on the magnetic field generated by a single coil and the fusion of low power magnetometers and inertial sensors. The fixed spatial relationship between the sensors is exploited to solve the ambiguity problem that occurs when only a single coil is employed. In this context a new positioning algorithm is developed and verified by simulations. Using a single coil instead of three coils as common in conventional magnetic field based systems brings a benefit both regarding power consumption and construction size. Moreover, as the proposed system requires no line of sight between source and sensors, it is ideally suited for portable, location-independent applications such as the mobile assessment of a persons' head to torso posture and movement e.g. for the purpose of examining causes for chronic pain in the cervical spine area under conditions of daily life.

This paper presents a method to increase the reliability of Unmanned Aerial Vehicle (UAV) sensor Fault Detection and Identification (FDI) in a multi-UAV context. Differential Global Positioning System (DGPS) and inertial sensors are used for sensor FDI in each UAV. The method uses additional position estimations that augment individual UAV FDI system. These additional estimations are obtained using images from the same planar scene taken from two different UAVs. Since accuracy and noise level of the estimation depends on several factors, dynamic replanning of the multi-UAV team can be used to obtain a better estimation in case of faults caused by slow growing errors of absolute position estimation that cannot be detected by using local FDI in the UAVs. Experimental results with data from two real UAVs are also presented.