Dead Reckoning Technique Research Articles

Combined effects of landscape fragmentation and sampling frequency of movement data on the assessment of landscape connectivity

BackgroundNetwork theory is largely applied in real-world systems to assess landscape connectivity using empirical or theoretical networks. Empirical networks are usually built from discontinuous individual movement trajectories without knowing the effect of relocation frequency on the assessment of landscape connectivity while theoretical networks generally rely on simple movement rules. We investigated the combined effects of relocation sampling frequency and landscape fragmentation on the assessment of landscape connectivity using simulated trajectories and empirical high-resolution (1 Hz) trajectories of Alpine ibex (Capra ibex). We also quantified the capacity of commonly used theoretical networks to accurately predict landscape connectivity from multiple movement processes.MethodsWe simulated forager trajectories from continuous correlated biased random walks in simulated landscapes with three levels of landscape fragmentation. High-resolution ibex trajectories were reconstructed using GPS-enabled multi-sensor biologging data and the dead-reckoning technique. For both simulated and empirical trajectories, we generated spatial networks from regularly resampled trajectories and assessed changes in their topology and information loss depending on the resampling frequency and landscape fragmentation. We finally built commonly used theoretical networks in the same landscapes and compared their predictions to actual connectivity.ResultsWe demonstrated that an accurate assessment of landscape connectivity can be severely hampered (e.g., up to 66% of undetected visited patches and 29% of spurious links) when the relocation frequency is too coarse compared to the temporal dynamics of animal movement. However, the level of landscape fragmentation and underlying movement processes can both mitigate the effect of relocation sampling frequency. We also showed that network topologies emerging from different movement behaviours and a wide range of landscape fragmentation were complex, and that commonly used theoretical networks accurately predicted only 30–50% of landscape connectivity in such environments.ConclusionsVery high-resolution trajectories were generally necessary to accurately identify complex network topologies and avoid the generation of spurious information on landscape connectivity. New technologies providing such high-resolution datasets over long periods should thus grow in the movement ecology sphere. In addition, commonly used theoretical models should be applied with caution to the study of landscape connectivity in real-world systems as they did not perform well as predictive tools.

Neural Networks-Based Wi-Fi/PDR Indoor Navigation Fusion Methods

Pedestrian dead-reckoning (PDR) technique has high accuracy in short-term positioning and is little affected by environments, but its cumulative error makes long-term positioning infeasible. Received signal strength (RSS) fingerprint positioning is immune to the cumulative error, but its poor radio signal propagation affected by indoor environments leads to a large positioning error. The extended Kalman filter (EKF) is widely applied to improve the two methods, but the approximation of nonlinearity still causes a large localization error. In this study, we propose a new approach based on neural networks for the Wi-Fi/PDR positioning fusion. The nonlinear mapping capability of back-propagation (BP) neural network is used to correct the EKF nonlinear approximation errors. Unlike the commonly used PDR algorithm, a series of pedestrian historical motion states are applied to train the long short-term memory (LSTM) and then make predictions. This method can significantly reduce cumulative errors in PDR. On-site experiments demonstrate that the proposed method achieves an average localization error of 1.18 m with 73% of the errors under 2 m, which outperforms EKF and unscented Kalman filter (UKF) by approximately 45% under the same test environment. Moreover, another fusion method using LSTM localization results as a priori information is described in this article. Different from the fusion at the resulting level of the previous method, this second method achieves the fusion at the algorithm level by limiting the Wi-Fi reference point (RP) search range through the LSTM localization results to reduce training and positioning time.

Tracking Control of Autonomous Underwater Vehicles with Acoustic Localization and Extended Kalman Filter

The absence of global positioning system (GPS) signals and the influence of ocean currents are two of the main challenges facing the autonomy of autonomous underwater vehicles (AUVs). This paper proposes an acoustic localization-based tracking control method for AUVs. Particularly, three buoys that emit acoustic signals periodically are deployed over the surface. Times of arrivals of these acoustic signals at the AUV are then obtained and used to calculate an estimated position of the AUV. Moreover, the uncertainties involved in the localization and ocean currents are handled together in the framework of the extended Kalman filter. To deal with system physical constraints, model predictive control relying on online repetitive optimizations is applied in the tracking controller design. Furthermore, due to the different sampling times between localization and control, the dead-reckoning technique is utilized considering detailed AUV dynamics. To avoid using the highly nonlinear and complicated AUV dynamics in the online optimizations, successive linearizations are employed to achieve a trade-off between computational complexity and control performance. Simulation results show that the proposed algorithms are effective and can achieve the AUV tracking control goals.

A Multi-Floor Indoor Pedestrian Localization Method Using Landmarks Detection for Different Holding Styles

The pedestrian dead reckoning (PDR) technique is widely used due to its ease of implementation on portable devices such as smartphones. However, the position error that accumulates over time is the main drawback of this technology. In this paper, we propose a fusion method combining a PDR technique and the landmark recognition methods for multi-floor indoor environments using a smartphone in different holding styles. The proposed method attempts to calibrate the position of a pedestrian by detecting whether the pedestrian passes by specific locations called landmarks. Three kinds of landmarks are defined, which are the WiFi, the turning, and the stairs landmarks, and the detection methods for each landmark are proposed. Besides, an adaptive floor detection method using a barometer and a WiFi fingerprinting technique is suggested for tracking a pedestrian in a multi-floor building. The developed system can track the pedestrian holding a smartphone in four styles. The results of the experiment conducted by three subjects changing the holding style in a three-floor building show the superior performance of the proposed method. It reduces the error rate of positioning results to less than 57.51% compared with the improved PDR alone system.

Crowdsourcing-Based Fingerprinting for Indoor Location in Multi-Storey Buildings

The number of available indoor location solutions has been growing, however with insufficient precision, high implementation costs or scalability limitations. As fingerprinting-based methods rely on ubiquitous information in buildings, the need for additional infrastructure is discarded. Still, the time-consuming manual process to acquire fingerprints limits their applicability in most scenarios. This paper proposes an algorithm for the automatic construction of environmental fingerprints on multi-storey buildings, leveraging the information sources available in each scenario. It relies on unlabelled crowdsourced data from users’ smartphones. With only the floor plans as input, a demand for most applications, we apply a multimodal approach that joins inertial data, local magnetic field and Wi-Fi signals to construct highly accurate fingerprints. Precise movement estimation is achieved regardless of smartphone usage through Deep Neural Networks, and the transition between floors detected from barometric data. Users’ trajectories obtained with Pedestrian Dead Reckoning techniques are partitioned into clusters with Wi-Fi measurements. Straight sections from the same cluster are then compared with subsequence Dynamic Time Warping to search for similarities. From the identified overlapping sections, a particle filter fits each trajectory into the building’s floor plans. From all successfully mapped routes, fingerprints labelled with physical locations are finally obtained. Experimental results from an office and a university building show that this solution constructs comparable fingerprints to those acquired manually, thus providing a useful tool for fingerprinting-based solutions automatic setup.

Finding a Parked Car Location in a Multi-Storey Building without GPS Service

<p class="0abstract">This paper introduces a new approach named ‘Find My Parked Car’ to provide the user with the return way for his parked car location in the indoor garage of a multi-storey building where GPS is denied using the acquisition data obtained from the inertial sensors on smart devices such as smartphones. We build up a system that employs Pedestrian Dead-Reckoning Technique (PDR) to track the user inside the building. This approach harness the environment landmarks for floors separation and the PDR paths’ landmarks to enhance them by applying the Kalman Filter. Then we prunes the paths to remove the redundant parts of paths. Finally, we display the paths that guide the user back to his car via an android application. The feature of our approach is to help users returning back to parked car location based only on the available ubiquitous sensors in the new smartphones without any need to have any map or any additional infrastructure support.</p>

Hybrid Onboard Smartphone Sensors Measurements to Improve Heading Estimation for Indoors Positioning Solutions

In the last decade, there is a significant progression and huge demand in using technology; specifically, those technologies are embedded in smartphones (SP). Examples of these technologies are embedding various sensors for multi-purposes. Positioning sensors (Accelerometer, Gyroscope, and Magnetometer) are one of the significant technologies. Besides this, indoor positioning services on smartphones are the main advantage of these sensors. There are many indoor positioning applications, for instance; billing, shopping, security and safety, indoor navigation, entertainment applications, and other point-of-interest (POI) applications. Nevertheless, precise position information through current positioning techniques is the main issue of these applications. The pedestrian dead reckoning (PDR) technique is one of the techniques in which the integration of onboard sensors is used for locating smartphones. Estimated distance, heading, and typical speed can be measured to determine the estimated position of the smartphone via using the PDR technique. The PDR technique offers a low positioning accuracy due to existing accumulated errors of the embedded sensors. To solve this issue, this article proposes a hybrid multi-sensors measurement to reduce the existing sensors drifts and errors and to increase estimated heading accuracy of the smartphone. Further, the sensors’ measurements with the previously estimated position are fused by using KALMAN Filter to determine the current location of the smartphone in each step of walking with better angular displacement accuracy. Proposed algorithm depends on increasing estimated angular displacement of the smartphone using combination of the integrated sensors’ measurements. The achieved positioning accuracy through the proposed approach and based on trial experiments is around 2 meters, which is equivalent to 10% improvement in comparison with state of the art.

Magnetic-Based Indoor Localization Using Smartphone via a Fusion Algorithm

The detection of indoor location based on magnetic fields collected by embedded sensors in smartphones has been progressed rapidly. Most current approaches rely on the particle filter (PF) scheme which combines the pedestrian dead reckoning (PDR) technique with patterns of previously recorded magnetic field intensity. The key challenges include inherent blindness and particle degradation problems. Here, a fusion algorithm combining the extended Kalman filter (EKF) and the PF scheme is proposed to address these issues. EKF is first used to reduce the possible location regions by fusing the PDR and magnetic field intensity results. The particle generation, update, and resampling processes are conducted afterward, and the final position is calculated by using the weighted mean of particles. As such, the blindness and particle degradation problems are alleviated by using particles in the reduced location regions at each processing step. Experiments show a localization accuracy of 1-2m when the user walks smoothly, which is better than those of traditional PF schemes, especially in cases under heavy magnetic distortions by using a reduced number of particles.

Integrating Moving Platforms in a SLAM Agorithm for Pedestrian Navigation.

In 3D pedestrian indoor navigation applications, position estimation based on inertial measurement units (IMUss) fails when moving platforms (MPs), such as escalators and elevators, are not properly implemented. In this work, we integrate the MPs in an upper 3D-simultaneous localization and mapping (SLAM) algorithm which is cascaded to the pedestrian dead-reckoning (PDR) technique. The step and heading measurements resulting from the PDR are fed to the SLAM that additionally estimates a map of the environment during the walk in order to reduce the remaining drift. For integrating MPs, we present a new proposal function for the particle filter implementation of the SLAM to account for the presence of MPs. In addition, a new weighting function for features such as escalators and elevators is developed and the features are learned and stored in the learned map. With this, locations of MPs are favored when revisiting the MPs again. The results show that the mean height error is about 0.1 m and the mean position error is less than 1 m for walks with long distances along the floors, even when using multiple floor level changes with different numbers of floors in a multistory environment. For walks with short walking distances and many floor level changes, the mean height error can be higher (about 0.5 m). The final floor number is in all cases except one correctly estimated.

Optimal path shape for range-only underwater target localization using a Wave Glider

Underwater localization using acoustic signals is one of the main components in a navigation system for an autonomous underwater vehicle (AUV) as a more accurate alternative to dead-reckoning techniques. Although different methods based on the idea of multiple beacons have been studied, other approaches use only one beacon, which reduces the system’s costs and deployment complexity. The inverse approach for single-beacon navigation is to use this method for target localization by an underwater or surface vehicle. In this paper, a method of range-only target localization using a Wave Glider is presented, for which simulations and sea tests have been conducted to determine optimal parameters to minimize acoustic energy use and search time, and to maximize location accuracy and precision. Finally, a field mission is presented, where a Benthic Rover (an autonomous seafloor vehicle) is localized and tracked using minimal human intervention. This mission shows, as an example, the power of using autonomous vehicles in collaboration for oceanographic research.

Toward the Unaided Estimation of Human Walking Speed Based on Sparse Modeling

The ability to track people’s movement (e.g., the human walking) is an important topic of interest in numerous research areas. State-of-the-art systems based on, e.g., dead reckoning, usually do not need any additional infrastructure. The disadvantage of the method is the poor accuracy because of measurement noises. The typical approach to noise cancellation is zero-velocity update (ZUPT). The method takes advantages of the cyclical nature of human walking. Thus some limitations of the ZUPT approach we propose the novel method based on transient artifact reduction algorithm (TARA) to mitigate these errors by noise estimation. TARA is the novel approach to estimate the low-frequency components in the signals by applying the concept of sparse optimization. The proposed algorithm was validated on synthetic and real signals. The tests for synthetic signals were performed for pulse signals with noise characterized by the Hurst exponent. In real signals studies, we applied the signals from three different data sets that were prepared to test the algorithm to human walking estimation. We found that TARA can estimate the underlying noise in the dead-reckoning-based systems. The results show that the TARA method can be used to improve the accuracy of human walking speed estimation as an alternative to the state-of-the-art method such as ZUPT. The results prove to be the important step toward unaided estimation of human walking velocity based on dead-reckoning technique.

Sensor Motes for the Exploration and Monitoring of Operational Pipelines

We report on a first field test in which miniaturized sensor motes were used to explore and inspect an operational pipeline by performing in situ measurements. The spherical sensor motes with a diameter of 39 mm were equipped with an inertial measurement unit (IMU) measuring 3-D acceleration, rotation, and magnetic field, as well as an ultrasound emitter. The motes were injected into the pipeline and traversed a 260-m section of it with the flow of water. After the extraction of the motes from the pipeline, the recorded IMU data were read out for the off-line analysis. Unlike dead-reckoning techniques, we analyze the IMU data to reveal structural information about the pipeline and locate pipe components, such as hydrants and junctions. The recorded data show different and distinct patterns that are a result of the fluid dynamics and the interaction with the pipeline. Using the magnetic data, pipe sections made from different materials and pipe components are identified and localized. A preliminary analysis on the motes’ interaction with the pipeline shows differences in pipe wall roughness and locates structural anomalies. The results of this field test show that sensor motes can be used as a versatile and cost-effective tool for exploration and inspection of a wide variety of pipelines.

Indoor Localization using Computer Vision and Visual-Inertial Odometry.

Indoor wayfinding is a major challenge for people with visual impairments, who are often unable to see visual cues such as informational signs, land-marks and structural features that people with normal vision rely on for wayfinding. We describe a novel indoor localization approach to facilitate wayfinding that uses a smartphone to combine computer vision and a dead reckoning technique known as visual-inertial odometry (VIO). The approach uses sign recognition to estimate the user's location on the map whenever a known sign is recognized, and VIO to track the user's movements when no sign is visible. The ad-vantages of our approach are (a) that it runs on a standard smartphone and re-quires no new physical infrastructure, just a digital 2D map of the indoor environment that includes the locations of signs in it; and (b) it allows the user to walk freely without having to actively search for signs with the smartphone (which is challenging for people with severe visual impairments). We report a formative study with four blind users demonstrating the feasibility of the approach and suggesting areas for future improvement.

Towards Arctic AUV Navigation

The navigational drift for Autonomous Underwater Vehicles (AUVs) operating in open ocean can be bounded by regular surfacing. However, this is not an option when operating under ice. To operate effectively under ice requires an on-board navigation solution that does not rely on external infrastructure. Moreover, some under-ice missions require long-endurance capabilities, extending the operating time of the AUVs from hours to days, or even weeks and months. This paper proposes a particle filter based terrain-aided navigation algorithm specifically designed to be implementable in real-time on the low-powered Autosub Long Range 1500 (ALR1500) vehicle to perform long-range missions, namely crossing the Artic Ocean. The filter performance is analysed using numerical simulations with respect to various key factors, e.g. of the sea-floor morphology, bathymetric update rate, map noise, etc. Despite very noisy on-board measurements, the simulation results demonstrate that the filter is able to keep the estimation error within the mission requirements, whereas estimates using dead-reckoning techniques experience unbounded error growth. We conclude that terrain-aided navigation has the potential to prolong underwater missions to a range of thousands of kilometres, provided the vehicle crosses areas with sufficient terrain variability and the model includes adequate representation of environmental conditions and motion disturbances.

Real time indoor localization integrating a model based pedestrian dead reckoning on smartphone and BLE beacons

Mobile and pervasive computing enabled a new realm of possibilities into the indoor positioning domain. Although many candidate technologies have been proposed, no one can still adapt to every use case. A case centered design and the implementation of the solution within the specific domain is the current research trend. With the rise of Bluetooth Low Energy (BLE) Beacons, i.e., platforms used to interact digitally with the real world, more standard positioning solutions are emerging in different contexts. However the reachable positioning accuracy with this technology is still unacceptable for some real applications (e.g., in the healthcare sector or the emergency management). In this paper, an hybrid localization application coupling a real time model based Pedestrian Dead Reckoning (PDR) technique and the analysis of the Received Signal Strength Indicator (RSSI) of BLE beacons is proposed. In particular, the smartphone application is composed by three main real time threads: a model based step length estimation, heading determination and the fusion of beacon information to reset the position and the drift error of the PDR. In order to give soundness to our approach we firstly validated the step length smartphone app with a stereo-photogrammetric system. The whole proposed solution was then tested on fifteen healthy subjects.

SmartMTra: Robust Indoor Trajectory Tracing Using Smartphones

Using smartphones for indoor motion trajectory tracing has attracted a lot of attention in recent years, which offers great potential to support a broad spectrum of applications in indoor environment, including elder care, business analysis, and navigation. Yet most existing approaches only work for certain pedestrian’s motion modes or smartphone’s carrying patterns, which lack the robustness and adaptability to general scenarios. In this paper, we propose SmartMTra, a comprehensive, robust, and accurate solution for indoor motion trajectory tracing based on smartphone’s built-in inertial sensors. Through analyzing the data from inertial sensors, we extract a set of features that are found to be highly related to human’s physical activities, which can help to identify motion mode and phone’s carrying pattern through a decomposition model. After that, SmartMTra utilizes the pedestrian dead reckoning technique, which involves estimating step counts, step-length, and heading direction, to achieve accurate trajectory tracing. We have conducted extensive experiments to evaluate the performance of SmartMTra in a campus building, and the results demonstrate the robustness of SmartMTra in various scenarios, as well as the superiority of SmartMTra over the state-of-the-art solutions.

An Indoor Positioning Method for Smartphones Using Landmarks and PDR.

Recently location based services (LBS) have become increasingly popular in indoor environments. Among these indoor positioning techniques providing LBS, a fusion approach combining WiFi-based and pedestrian dead reckoning (PDR) techniques is drawing more and more attention of researchers. Although this fusion method performs well in some cases, it still has some limitations, such as heavy computation and inconvenience for real-time use. In this work, we study map information of a given indoor environment, analyze variations of WiFi received signal strength (RSS), define several kinds of indoor landmarks, and then utilize these landmarks to correct accumulated errors derived from PDR. This fusion scheme, called Landmark-aided PDR (LaP), is proved to be light-weight and suitable for real-time implementation by running an Android application designed for the experiment. We compared LaP with other PDR-based fusion approaches. Experimental results show that the proposed scheme can achieve a significant improvement with an average accuracy of 2.17 m.

Mobile Crowdsensing Framework for a Large-Scale Wi-Fi Fingerprinting System

Although Wi-Fi fingerprinting systems enable location-based services in indoor environments, the service coverage isn't scalable due to costly site surveys. Mobile crowdsensing (MCS), where casual smartphone users conduct the site survey, has recently emerged as a promising solution. Applying MCS to a system introduces new challenges: motivating active participation, inferring location information of unlabeled fingerprints, and managing a large amount of fingerprints. The proposed MCS framework obtains fingerprints from users by exploiting social network service applications and using the pedestrian dead reckoning technique. MCS accurately infers the location information of unlabeled fingerprints using physical-layout and signal-strength measurements. The framework also selects an optimal set of fingerprints, which introduces high accuracy with a slightly increased database size. This case study shows the feasibility of the proposed MCS framework.

V2V-EN – Vehicle-2-Vehicle Elastic Network

Vehicle2Vehicle (V2V) is an emerging and fruitful area of research widely investigated in IoT and in the driverless setting, in particular. It allows cars to establish ad-hoc networks to cooperate in order to share information and therefore improve the safety of the drivers. A fundamental aspect in V2V is the positioning accuracy. While in several outdoor scenarios modern Global Navigation Satellite Systems (GNSS) are sufficiently precise, they are less reliable in indoor situations. This is undesirable as often cars move from outdoor to indoor scenarios and vice versa (think for example to cars leaving a box, or entering a tunnel). For this reason large research effort has been recently devoted to integrate GNSS systems in order to improve indoor positioning accuracy.In this paper we present Vehicle-2-Vehicle Elastic Network, a novel metodology that allows accurate vehicular indoor positioning based on a dead-reckoning technique via an Indoor Inertial Navigation System, aided by an elastic graph generated by a V2V-WiFi system. The received signal strength (RSS) from nearby cars is detected, and is used to improve the location via RSS-to distance information crowdsourcing.

Robust Indoor Positioning With Lifelong Learning

Inertial tracking and navigation systems have been playing an increasingly important role in indoor tracking and navigation. They have the competitive advantage of leveraging not requiring expensive infrastructure—only existing smart mobile devices with embedded inertial measurement units. When aided with other sources of information, such as radio data from existing WiFi/BLE infrastructure, and environment constraints from floor plans or radio maps, they often report great performance of 0.5–2 m. Given the promising results, what is it that prevents the widespread adoption of this tracking solution? We argue that pedestrian dead reckoning (PDR) techniques are often evaluated in a specific context and are not mature enough to handle variations in user motion, device type, device placement, or environment. They typically use a number of parameters that require careful context-specific tuning, which is labor intensive and requires expert knowledge. In this paper, we propose two novel approaches to address these problems. Our first contribution is a robust PDR algorithm, which is based on general physics principles that underpin human motion and is by design robust to context changes. The second contribution is a novel way of interaction between the PDR and map matching layers based on the principle of lifelong learning. Unlike traditional approaches where information flows unidirectionally from the PDR to the map matching layer, we introduce a feedback loop that can be used to automatically tune the parameters of the PDR algorithm. This is not dissimilar to the way that people improve their navigation skills when they repeatedly visit the same environment. Extensive experiments in multiple sites, with a variety of users, devices, and device placements, show that the combination of a robust PDR with a lifelong learning tracker can achieve submeter accuracy with no user effort for parameter tuning.