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Satellite navigation is the backbone of maritime navigation today. However, the technical vulnerability of on-board Global Navigation Satellite System (GNSS) receivers the satellite system greatly destabilizes maritime security due to the loss of ship's position and accurate time. This article devoted to study an alternative method for obtaining coordinates and accurate time based on the use of automatic identification system (AIS) radio channels, so-called range mode (R-Mode). We use other AIS ship stations with reliable position data as reference stations and determine time of arrival for received AIS transmissions. To improve the accuracy of measuring signal arrival instance in the time difference of arrival (TDOA), that we utilize for trilateration, it is proposed signal oversampling and applying the fast Fourier transform (FFT) to the product of quadrature components of the baseband Gaussian minimum shift keying (GMSK) signal in the window of AIS time slot. To take into account the movement of other ships, appropriate coordinate corrections are foreseen, which can be calculated by dead reckoning or by the inertial navigation system of our ship. The proposed method is fully compatible with the existing AIS signals and may be employed in critical situations of locally limited (jamming, spoofing) GNSS abilities. It can be implemented as a separate unit, working for receiving in parallel with the mandatory AIS transponder. [ABSTRACT FROM AUTHOR]

The method of multi-sensor integrated navigation improves navigation accuracy by fusing various sensor data. However, when a sensor is disturbed or malfunctions, incorrect measurement information will seriously affect the estimation of the trajectory, which will lead to a decrease in accuracy. Existing factor graph models based on weights can neither fully resist the influence of disturbances nor guarantee the local rationality of estimated trajectories. In this paper, a factor graph with local constraints model that fuses the magnetic field and pedestrian dead reckoning data is proposed to navigate complex curved trajectories. First, adding local constraints to the pedestrian dead reckoning measurement converts the navigation solution problem into a hard-constrained nonlinear least squares problem. Then, a mapping model is constructed to reconstruct the variable space and the Adam gradient algorithm is used to realize a fast calculation. The navigation accuracy of this algorithm is better than that of the state-of-the-art method in real-world experiments, with an average accuracy of 0.83 m. [ABSTRACT FROM AUTHOR]

We propose a sensor fusion method of multiple inertial measurement units (IMU) with different resolutions to reduce quantization errors and improve the measurement accuracy of dead reckoning navigation. The resolution and dynamic range of the accelerometer within MEMS IMU have a trade-off relationship; the selection of a fixed dynamic range in a single IMU may increase the error of the inertial navigation system. We integrate multiple accelerometer signals with different resolutions into a weighted average that depends on the measurements. This reduces the quantization error by creating an optimal resolution tailored to the specific measurement range. To verify the proposed algorithm, we examined the effect of mathematically generated accelerometer values. [ABSTRACT FROM AUTHOR]

This article proposes the bearing, elevation, and depth difference (BEDD) passive inverted super short baseline (piSSBL) acoustic navigation method for autonomous underwater vehicles (AUVs). The piSSBL acoustic positioning is scalable regarding the number of receiving vehicles, and the BEDD measurements do not require accurate intervehicle time synchronization. The proposed observer combines acoustic positioning with dead reckoning navigation using particle filter localization. The experimental evaluation confirmed that the observer tracks an AUV's north and east positions by recursively repeating the BEDD observations, and it tended to converge when the system satisfied the observability conditions. [ABSTRACT FROM AUTHOR]

The Open Access movement has gathered significant momentum over the last couple of years. This has been instigated largely by cOAlition S and those funders which support its aims. Is 'Read and Publish' the way forward? Will it work for all publishers? All authors? All subscribers? All readers? This article looks at the history of OA and updates a similar piece from 2013. A detailed glossary of terms is given at the end of the article. [ABSTRACT FROM AUTHOR]

We have developed a human-navigation-inspired and semantic map-building for communication between human and wireless robots. Four human navigation strategies (dead reckoning, egocentric place recognition, and reorientation) are integrated in our proposed method as an egocentric-semantic topology representation, egocentric measurement representation, and reorientation rules, respectively. In experiments, we successfully created a semantic representation and estimated the location of the robot by changing inaccurate distance and angle to approximate measurements using our proposed computing method. Semantic representation building took place in a 14 × 26.5 m corridor. The proposed method enables a robot to navigate effectively in a service environment using navigation schemes inspired by humans and their actions. [ABSTRACT FROM AUTHOR]

In this research, a hybrid dead reckoning error correction scheme is developed based on extended Kalman filter (EKF) and map matching (MM) to improve the positioning accuracy for vehicle self-localization. The developed method aims at obtaining accurate positions when the GPS signals are occasionally unavailable or weakened. First, the heading data collected from an odometer and an optical fiber gyroscope are integrated by an EKF to reduce the random errors in dead reckoning. Then a modified topological MM algorithm is developed to reduce the systematic errors in dead reckoning. In this work, both cross-track errors and along-track errors are considered to improve positioning accuracy of MM. The errors are finally corrected using the results achieved from both the dead reckoning and the MM when the driving distance of a vehicle exceeds a predefined length or the vehicle turns in an intersection. Experiments have been conducted to evaluate the developed method and the results show that the maximum error and average error of dead reckoning can be respectively reduced to 15.4 m and 5.2 m during the experiment with total distance of 43 km. This positioning accuracy is even better than the accuracy of the low-cost GPSs which are usually at the order of 15-20 m (95%). The developed method is effective to achieve the positions of the vehicle when the GPS signals are occasionally unavailable or weakened. [ABSTRACT FROM AUTHOR]

Highlights • Generally, Pedestrian Dead Reckoning (PDR) system with a foot mounted inertial sensors uses zero-velocity update (ZUPT). • ZUPT is not suitable for an irregular walk because the velocity is not small enough to assume zero during the stance phase. • Proposed PDR system uses a predicted velocity at the contact phase where a shoe touches the ground. • An ellipsoidal shoe modeling predicts the nonzero velocity under kinematic constraint. • The performance of the proposed algorithm is verified by experimental results with various motions. Abstract In this paper, we propose a pedestrian dead reckoning (PDR) system that uses the velocity predicted by a foot kinematic constraint and ellipsoidal shoe modeling in the contact phase. Generally, a PDR system with foot-mounted inertial sensors often uses zero-velocity update (ZUPT) to reduce the influence of the bias and white noise in the gyroscope and accelerometer signals. However, when a pedestrian walks irregularly, ZUPT is unsuitable because the velocity is not small enough to assume that it is zero during the stance phase. Unlike a conventional PDR system that uses ZUPT in the stance phase when the shoe has completely stopped, the proposed PDR system uses the predicted velocity in the contact phase when the shoe touches the ground from the heel-strike to the toe-off states by employing the lever arm vector and the angular velocity to predict the nonzero velocity under the kinematic constraint. The lever arm vector is obtained by the attitude of the shoe according to ellipsoidal shoe modeling. The predicted velocity is used as a measure of the filter during the contact phase. The proposed PDR system can be utilized in various motions with nonzero velocity in the contact phase. The performance of the algorithm was verified by experimental results with various motions. [ABSTRACT FROM AUTHOR]

Accumulative errors can be retained all the time when classical Kalman filtering is adopted for odometer-based dead reckoning, thereby affecting self-localization accuracy of the robot. A mobile robot self-localization method based on limited memory Kalman filtering (LMKF) with exponential fading factor was proposed to reduce accumulative errors of the odometer and improve localization accuracy of the mobile robot. The self-localization system of mobile robot was built. A mathematical model was established based on LMKF with exponential fading factor. A dead reckoning method fusing multi-sensor information was proposed. The model accuracy was verified through simulation and test. Results indicate that the LMKF method with exponential fading factor positively affects the tracking of high-speed maneuvering dynamic targets, and its localization error is reduced by 42.5% compared with the odometer-based dead reckoning. The tracking accuracy of the mobile robot is stable at 0.5 m. This study can provide references for mobile robot selflocalization using multi-sensor. [ABSTRACT FROM AUTHOR]

Buoyancy-driven underwater gliders are considered to be advanced platforms for large-scale ocean exploration. However, it is greatly affected by ocean currents, and traditional dead reckoning navigation methods are inadequate in accurately predicting its own position, causing great difficulties in underwater target detection. To solve this problem, a navigation estimation method based on the current prediction model and a convolutional neural network-long short-term memory hybrid neural network is proposed in this paper to predict the position of underwater gliders. Analysis of the experimental results shows that the hybrid neural network model trained with underwater glider sea trial data and simulated motion data can predict the glider speed more accurately than the current-assisted dead reckoning navigation, and can predict the relative position of the glider more accurately with the updated feedback from the current prediction model. The test results show that the data-driven prediction method can greatly help to predict the position of underwater gliders in the absence of other underwater positioning and navigation equipment. • A hybrid neural network to predict the position of the underwater glider. • Underwater glider position prediction under the influence of water currents. • Predicting the underwater position in the absence of other equipment. [ABSTRACT FROM AUTHOR]

Studies on indoor positioning systems have typically used received signal strength indicators (RSSIs) for positioning; RSSI offers the advantage of low calculation complexity, but indoor environments frequently cause RSSI to produce large errors. Improving the RSSI positioning accuracy requires deploying a large number of reference nodes, which increases both the cost and the computational complexity of RSSI. Because of the progress of technology, some sensors now feature high precision and low manufacturing cost. Therefore, a variety of inertial sensor components are commonly applied, and numerous studies on positioning systems have avoided RSSIs and have used inertial sensors to achieve indoor positioning by constructing various dead reckoning (DR) systems. Inertial sensors typically suffer from cumulative errors. This study proposes a fuzzy control method to combine DR and RSSI systems, to produce a positioning system with low positioning error. DR positioning can produce superior positioning accuracy, and the RSSI method does not generate cumulative errors; thus the proposed system solves the cumulative error problem that plagues DR systems. Compared with other positioning technologies, the method proposed by this study can be applied prevalently; one need not consider the environment for deployment of reference nodes. This reduces the complexity of the deployment of a positioning system. In addition, when the angular deviation for the direction of motion of the DR system was large, the proposed method had superior correction performance. With the proposed fuzzy control method, the average positioning error was 0.625 m, which was 73.8% more accurate than the average positioning error obtained using the conventional DR method. [ABSTRACT FROM AUTHOR]

We study the indoor localization problem based on Pedestrian Dead Reckoning (PDR) by analyzing the causes of localization error during pedestrian walking. To optimize the PDR-based localization method, we firstly propose a step-sense indoor localization framework, namely, Stepsense, which can analyze the total acceleration of the accelerometer sensor and obtain the number of pedestrian steps using the peak detection. In the Stepsense framework, the step length is calculated by the difference between the acceleration peak and the trough. The 9DOF (Degree of Freedom) and 6DOF methods are invoked by double-check strategy to make the result of direction estimation more accurate. Secondly, the adaptive error model is used to correct the state of particles in the particle filter, in which map matching and RSS matching are integrated. Finally, both the Stepsense framework and the proposed fusion localization method are examined in detail through experiments. [ABSTRACT FROM AUTHOR]

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A fundamental aspect of biological intelligence, from microbes to megafauna, is the ability to forage for the provisions required to sustain life. This sometimes underrated ability to seek out, identify, and use objects of interest in an environment with limited prior knowledge is an important capability that is also needed by robots. Many robotics applications can be modeled as foraging problems, such as search and rescue, wildlife tracking, crop pollination and harvesting, mining and in-situ-resource utilization, and scientific data/sample collection. [ABSTRACT FROM AUTHOR]

This research proposes a novel in-motion fine alignment algorithm for vehicular dead reckoning (DR) with odometer-aided strapdown inertial navigation system (SINS) while the map matching result is used for a group of landmark points to estimate misalignment angles. The proposed algorithm is designed based on principle of similarity, that is, trajectory of DR is similar to the true trajectory that the main difference between these two trajectories is rotation and scale. Further, the results from map matching are introduced as a group of landmark points to estimate the residual of azimuth error angle after coarse alignment and the scale factor error of the odometer. It is theoretically proved that the alignment effectiveness based on the results from map matching is equivalent to that on single zero error landmark point. Finally, digital simulations are conducted to verify the presented algorithm and test the performance. [ABSTRACT FROM AUTHOR]

In order to autonomously transfer from one point of the environment to the other, Autonomous Underwater Vehicles (AUV) need a navigational system. While navigating underwater the vehicles usually use a dead reckoning method which calculates vehicle movement on the basis of the information about velocity (sometimes also acceleration) and course (heading) provided by on-board devicesl ike Doppler Velocity Logs and Fibre Optical Gyroscopes. Due to inaccuracies of the devices and the influence of environmental forces, the position generated by the dead reckoning navigational system (DRNS) is not free from errors, moreover the errors grow exponentially in time. The problem becomes even more serious when we deal with small AUVs which do not have any speedometer on board and whose course measurement device is inaccurate. To improve indications of the DRNS the vehicle can emerge onto the surface from time to time, record its GPS position, and measure position error which can be further used to estimate environmental influence and inaccuracies caused by mechanisms of the vehicle. This paper reports simulation tests which were performed to determine the most effective method for correction of DRNS designed for a real Biomimetic AUV. [ABSTRACT FROM AUTHOR]

Pedestrian dead reckoning (PDR) positioning algorithms can be used to obtain a target's location only for movement with step features and not for driving, for which the trilateral Bluetooth indoor positioning method can be used. In this study, to obtain the precise locations of different states (pedestrian/car) using the corresponding positioning algorithms, we propose an adaptive method for switching between the PDR and car indoor positioning algorithms based on multilayer time sequences (MTSs). MTSs, which consider the behavior context, comprise two main aspects: filtering of noisy data in small-scale time sequences and using a state chain to reduce the time delay of algorithm switching in large-scale time sequences. The proposed method can be expected to realize the recognition of stationary, walking, driving, or other states; switch to the correct indoor positioning algorithm; and improve the accuracy of localization compared to using a single positioning algorithm. Our experiments show that the recognition of static, walking, driving, and other states improves by 5.5%, 45.47%, 26.23%, and 21% on average, respectively, compared with convolutional neural network (CNN) method. The time delay decreases by approximately 0.5-8.5 s for the transition between states and by approximately 24 s for the entire process. [ABSTRACT FROM AUTHOR]

Abstract: The ARTEMIS docking system demonstrates autonomous docking capability applicable to robotic exploration of sub‐ice oceans and sub‐glacial lakes on planetary bodies, as well as here on Earth. In these applications, melted or drilled vertical access shafts restrict vehicle geometry as well as the in‐water infrastructure that may be deployed. The ability of the vehicle to return reliably and precisely to the access point is critical for data return, battery charging, and/or vehicle recovery. This paper presents the mechanical, sensor, and software components that make up the ARTEMIS docking system, as well as results from field deployment of the system to McMurdo Sound, Antarctica in the austral spring of 2015. The mechanical design of the system allows the vehicle to approach the dock from any direction and to pitch up after docking for recovery through a vertical access shaft. It uses only a small volume of in‐water equipment and may be deployed through a narrow vertical access shaft. The software of the system reduces position estimation error with a hierarchical combination of dead reckoning, acoustic aiding, and machine vision. The system provides critical operational robustness, enabling the vehicle to return autonomously and precisely to the access shaft and latch to the dock with no operator input. [ABSTRACT FROM AUTHOR]

Accurately determining pedestrian location in indoor environments using consumer smartphones is a significant step in the development of ubiquitous localization services. Many different map-matching methods have been combined with pedestrian dead reckoning (PDR) to achieve low-cost and bias-free pedestrian tracking. However, this works only in areas with dense map constraints and the error accumulates in open areas. In order to achieve reliable localization without map constraints, an improved image-based localization aided pedestrian trajectory estimation method is proposed in this paper. The image-based localization recovers the pose of the camera from the 2D-3D correspondences between the 2D image positions and the 3D points of the scene model, previously reconstructed by a structure-from-motion (SfM) pipeline. This enables us to determine the initial location and eliminate the accumulative error of PDR when an image is successfully registered. However, the image is not always registered since the traditional 2D-to-3D matching rejects more and more correct matches when the scene becomes large. We thus adopt a robust image registration strategy that recovers initially unregistered images by integrating 3D-to-2D search. In the process, the visibility and co-visibility information is adopted to improve the efficiency when searching for the correspondences from both sides. The performance of the proposed method was evaluated through several experiments and the results demonstrate that it can offer highly acceptable pedestrian localization results in long-term tracking, with an error of only 0.56 m, without the need for dedicated infrastructures. [ABSTRACT FROM AUTHOR]

The challenges of self-contained sensor based pedestrian dead reckoning (PDR) are mainly sensor installation errors and path integral errors caused by sensor variance, and both may dramatically decrease the accuracy of PDR. To address these challenges, this paper presents a multi-sensor fusion based method in which subjects perform specified walking trials at self-administered speeds in both indoor and outdoor scenarios. After an initial calibration with the reduced installation error, quaternion notation is used to represent three-dimensional orientation and an extend Kalman filter (EKF) is deployed to fuse different types of data. A clustering algorithm is proposed to accurately distinguish stance phases, during which integral error can be minimized using Zero Velocity Updates (ZVU) method. The performance of proposed PDR method is evaluated and validated by an optical motion tracking system on healthy subjects. The position estimation accuracy, stride length and foot angle estimation error are studied. Experimental results demonstrate that the proposed self-contained inertial/magnetic sensor based method is capable of providing consistent beacon-free PDR in different scenarios, achieving less than 1% distance error and end-to-end position error. [ABSTRACT FROM AUTHOR]

This paper proposes a method for tight integration of IMU (Inertial Measurement Unit), stereo VO (Visual Odometry) and digital map for land vehicle navigation, which effectively limits the quick drift of DR (Dead Reckoning) navigation system. In this method, the INS provides the dynamic information of the land vehicle, which is used to predict the position and attitude of cameras in order to obtain the predicted pixel coordinates of features on the image. The difference between the measured and predicted pixel coordinates is used to reduce the accumulated errors of INS. To implement the proposed method, an Extended Kalman filter (EKF) is first used to integrate the inertial and visual sensor data. The integrated solution of position, velocity and azimuth is then applied by fuzzy logic map matching (MM) to project the vehicle location on the correct road link. The projected position on the road link and the road link azimuth can finally be used to reduce the dead reckoning drifts. In this way, the accumulated system errors can be significantly reduced. The testing results indicate that the horizontal RMSE (root-mean-square-error) of the proposed method is less than 20 meters over a traveled distance of five kilometers and the relative horizontal error is below 0.4 percent. [ABSTRACT FROM AUTHOR]

Different calculation methods and configurations of navigation systems can be used in algorithms of navigational parameter fusion and estimation. The article presents a comparison of two methods of fusion of dead reckoning position with that from a positioning system. These are the least squares method and the Kalman filter. In both methods the minimization of the sum of squared measurement deviations is the optimization criterion. Both methods of navigation position parameter measurements fusion are illustrated using the data recorded during actual sea trials. With the same probabilistic model of dead reckoning navigation, the fusion of DR results with positioning data gives similar outcome. [ABSTRACT FROM AUTHOR]

The motivations, the design, and some applications of the new Pedestrian Dead Reckoning (PDR) navigation device, ULISS (Ubiquitous Localization with Inertial Sensors and Satellites), are presented in this paper. It is an original device conceived to follow the European recommendation of privacy by design to protect location data which opens new research toward self-contained pedestrian navigation approaches. Its application is presented with an enhanced PDR algorithm to estimate pedestrian’s footpaths in an autonomous manner irrespective of the handheld device carrying mode: texting or swinging. An analysis of real-time coding issues toward a demonstrator is also conducted. Indoor experiments, conducted with 3 persons, give a 5.8% mean positioning error over the 3 km travelled distances. [ABSTRACT FROM AUTHOR]

Autonomous driving in public roads requires precise localization within the range of few centimeters. Even the best current precise localization system based on the Global Navigation Satellite System (GNSS) can not always reach this level of precision, especially in an urban environment, where the signal is disturbed by surrounding buildings and artifacts. Laser range finder and stereo vision have been successfully used for obstacle detection, mapping and localization to solve the autonomous driving problem. Unfortunately, Light Detection and Ranging (LIDARs) are very expensive sensors and stereo vision requires powerful dedicated hardware to process the cameras information. In this context, this article presents a low-cost architecture of sensors and data fusion algorithm capable of autonomous driving in narrow two-way roads. Our approach exploits a combination of a short-range visual lane marking detector and a dead reckoning system to build a long and precise perception of the lane markings in the vehicle's backwards. This information is used to localize the vehicle in a map, that also contains the reference trajectory for autonomous driving. Experimental results show the successful application of the proposed system on a real autonomous driving situation. [ABSTRACT FROM AUTHOR]

In recent years, indoor localization systems have been the object of significant research activity and of growing interest for their great expected social impact and their impressive business potential. Application areas include tracking and navigation, activity monitoring, personalized advertising, Active and Assisted Living (AAL), traceability, Internet of Things (IoT) networks, and Home-land Security. In spite of the numerous research advances and the great industrial interest, no canned solutions have yet been defined. The diversity and heterogeneity of applications, scenarios, sensor and user requirements, make it difficult to create uniform solutions. From that diverse reality, a main problem is derived that consists in the lack of a consensus both in terms of the metrics and the procedures used to measure the performance of the different indoor localization and navigation proposals. This paper introduces the general lines of the EvAAL benchmarking framework, which is aimed at a fair comparison of indoor positioning systems through a challenging competition under complex, realistic conditions. To evaluate the framework capabilities, we show how it was used in the 2016 Indoor Positioning and Indoor Navigation (IPIN) Competition. The 2016 IPIN competition considered three different scenario dimensions, with a variety of use cases: (1) pedestrian versus robotic navigation, (2) smartphones versus custom hardware usage and (3) real-time positioning versus off-line post-processing. A total of four competition tracks were evaluated under the same EvAAL benchmark framework in order to validate its potential to become a standard for evaluating indoor localization solutions. The experience gained during the competition and feedback from track organizers and competitors showed that the EvAAL framework is flexible enough to successfully fit the very different tracks and appears adequate to compare indoor positioning systems. [ABSTRACT FROM AUTHOR]

Vision based pedestrian tracking becomes a hard problem when long-term/heavy occlusion happens or pedestrian temporarily moves out of the visual field. In this paper, a novel persistent pedestrian tracking system is presented which combines visual signal from surveillance cameras and sensor signals from Inertial Measurement Unit (IMU) carried by pedestrians themselves. IMU tracking performs Dead Reckoning (DR) approach utilizing accelerometer, gyroscope and magnetometer. IMU tracking has nothing to do with visual occlusion, so it keeps working even when pedestrians are visually occluded. Meanwhile, visual tracking assists in calibrating IMU to avoid the bias drift during DR. The experimental results show that the IMU and visual tracking are complementary to each other and their combination performs robust pedestrian tracking in many challenging scenarios. [ABSTRACT FROM AUTHOR]

Recently, to improve safety and convenience in driving, numerous sensors are mounted on cars to operate advanced driver assistant systems. Among various sensors, vehicle dynamic sensors can measure the vehicle motions such as speed and rotational angular speed for dead reckoning, which can be applied to develop a land vehicle positioning system to overcome the weaknesses of the GNSS technique. In this paper, three land vehicle positioning algorithms that integrate GNSS with vehicle dynamic sensors including a wheel speed sensor (WSS), a yaw rate sensor (YRS), and a steering angle sensor (SAS) are implemented, and then a performance evaluation was conducted during GNSS outages. Using a loosely coupled strategy, three integration algorithms are designed, namely, GNSS/WSS, GNSS/WSS/YRS, and GNSS/WSS/YRS/SAS. The performance of the three types of integration algorithm is evaluated based on two data sets. The results indicate that both the GNSS/WSS/YRS integration and the GNSS/WSS/YRS/SAS integration could estimate the horizontal position with meter-level accuracy during 30-second GNSS outages. However, the GNSS/WSS integration would provide an unstable navigation solution during GNSS outages due to the accuracy limitation of the computed yaw rate using WSS. [ABSTRACT FROM AUTHOR]

In order to obtain the accurate velocity of a vehicle, calibration of a laser Doppler velocimeter (LDV) is particularly important after installation of the inertial navigation system (INS) and LDV. The calibration accuracy determines the navigational accuracy of the INS/LDV integrated navigation system. In this paper, a highly accurate calibration method based on the error equations of the INS/LDV integrated system is presented. The basic principles of analytic calibration and filtering calibration methods are discussed in detail. The test results show that the scale factor and the installation error of the LDV can be automatically calibrated by our novel calibration method with sufficient accuracy. For a high-precision INS and a 2-D LDV integrated navigation system, after calibration, the position error of dead reckoning (DR) decreased from 1180 m in 2 h to 8 m in 2 h. For a medium precision INS and a 1-D LDV integrated navigation system, after calibration, the position error of DR decreased from 1300 m in 1 h to 25 m in 1 h. The excellent parameter stability of the LDV is also illustrated. [ABSTRACT FROM AUTHOR]

This paper is intended to provide an outline of how the lead and line formed a vital, but often overlooked, role in safe navigation during the 16th Century, with some comments on its later use up to, and into the 20th Century. During this period, the development of the more mathematical dead-reckoning and the more exciting astro-navigation attracted the attention of the literate commentators who were responsible for record keeping while the often illiterate sailors got on with practising their craft. Also, May (1970), has commented that, “the navigators during the great age of discovery must have made extensive use of the lead and line, but references are extremely sparse, for all their rough log-books were destroyed and only the most important references to their navigational proceedings were copied into the fair accounts of their voyages which have come down to us.” [ABSTRACT FROM AUTHOR]

The traditional Dead Reckoning algorithm predicts the future motion state based on a determined polynomial predictor, and the forecasting performance would vary with different types of motion entities. This paper proposes an enhanced dead reckoning algorithm based on hybrid extrapolation models, which can be used to reduce the communication in a distributed interactive simulation. The proposed algorithm perform extrapolation using a number of candidate predictors. Its idea is based on the assumption that a complex trajectory can be decomposed into several simple trajectories. The experimental evaluations show that the enhanced Dead Reckoning algorithm provides better performance in correction data reduction and accurate estimation. [ABSTRACT FROM AUTHOR]

The organization of rodent exploratory behavior appears to depend on self-movement cue processing. As of yet, however, no studies have directly examined the vestibular system’s contribution to the organization of exploratory movement. The current study sequentially segmented open field behavior into progressions and stops in order to characterize differences in movement organization between control and otoconia-deficient tilted mice under conditions with and without access to visual cues. Under completely dark conditions, tilted mice exhibited similar distance traveled and stop times overall, but had significantly more circuitous progressions, larger changes in heading between progressions, and less stable clustering of home bases, relative to control mice. In light conditions, control and tilted mice were similar on all measures except for the change in heading between progressions. This pattern of results is consistent with otoconia-deficient tilted mice using visual cues to compensate for impaired self-movement cue processing. This work provides the first empirical evidence that signals from the otolithic organs mediate the organization of exploratory behavior, based on a novel assessment of spatial orientation. [ABSTRACT FROM AUTHOR]

In a waist-worn Pedestrian Navigation System (PNS) based on Dead-Reckoning (DR), heading drift caused by Micro-Electro-Mechanical System (MEMS) gyro bias is an essential factor affecting DR accuracy. Considering the characteristics of pedestrian navigation and the poor bias repeatability of MEMS gyros, this paper presents a standing calibration method for MEMS gyro bias. The current gyro biases for each boot can be calibrated accurately in the initial stage before walking. Since the attitude angles calculated by the output data from magnetic sensor and accelerometers do not drift, this paper applies the reverse algorithm of attitude updating to calculate the angular velocities of human motion. Then the gyro biases at each moment can be acquired by subtraction operation between the measured angular velocities from gyros and the calculated angular velocities of human motion. Finally, in order to restrain the random error caused by sensor noise, the calculated biases in the initial stage are smoothed, and consequently the optimal estimate of current gyro biases after each boot can be obtained. Still and dynamic turntable experiments and a walking experiment are performed to compare and analyse the proposed method and the Zero Angular Rate Update (ZARU) method. Experimental results show that the proposed method can also calibrate the gyro bias accurately in the case of body swaying. [ABSTRACT FROM AUTHOR]

Dead reckoning is an important aspect of estimating the instantaneous position of a mobile robot. An inertial measurement unit (IMU) is generally used for dead reckoning because it measures triaxis acceleration and triaxis angular velocities in order to estimate the position of the mobile robot. Positioning with inertial data is reasonable for a short period of time. However, the velocity, position, and attitude errors increase over time. Much research has been conducted in ways to reduce these errors. To position a mobile robot, an absolute positioning method can be combined with dead reckoning. The performance of a combined positioning method can be improved based on improvement in dead reckoning. In this paper, an ultrasonic anemometer is used to improve the performance of dead reckoning when indoors. A new approach to the equation of an ultrasonic anemometer is proposed. The ultrasonic anemometer prevents divergence of the mobile robot’s velocity. To position a mobile robot indoors, the ultrasonic anemometer measures the relative movement of air while the robot moves through static air. Velocity data from the ultrasonic anemometer and the acceleration and angular velocity data from the IMU are combined via Kalman filter. Finally we show that the proposed method has the performance with a positioning method using encoders on a good floor condition. [ABSTRACT FROM AUTHOR]

This paper proposes a dead-reckoning (DR)/WiFi fingerprinting/magnetic matching (MM) integration structure that uses off-the-shelf sensors in consumer portable devices and existing WiFi infrastructures. One key improvement of this structure over previous DR/WiFi/MM fusion structures is the introduction of a three-level quality-control (QC) mechanism based on the interaction between different techniques. On QC Level #1, several criteria are applied to filter out blunders or unreliable measurements in each separate technology. Then, on Level #2, a threshold-based approach is used to set the weight of WiFi results automatically through the investigation of the EKF innovation sequence. Finally, on Level #3, DR/WiFi results are utilized to limit the MM search space and in turn reduce both mismatch rate and computational load. The proposed structure reduced the root mean square (RMS) of position errors in the range of 13.3 to 55.2% in walking experiments with two smartphones, under four motion conditions, and in two indoor environments. Furthermore, the proposed structure reduced the rate of mismatches (i.e., matching to an incorrect point that is geographically located over 15 m away from the true position) rate by over 75.0% when compared with previous DR/WiFi/MM integration structures. [ABSTRACT FROM AUTHOR]

Smartphones have become indispensable in our daily lives. Their various embedded sensors have inspired innovations in mobile applications--especially for indoor navigation. However, the accuracy, reliability and generalizability of navigation all continue to struggle in environments lacking a Global Navigation Satellite System (GNSS). Pedestrian Dead Reckoning (PDR) is a popular method for indoor pedestrian navigation. Unfortunately, due to its fundamental principles, even a small navigation error will amplify itself, step by step, generally leading to the need for supplementary resources to maintain navigation accuracy. Virtually all mobile devices and most robots contain a basic camera sensor, which has led to the popularity of image-based localization, and vice versa. However, all of the image-based localization requires continuous images for uninterrupted positioning. Furthermore, the solutions provided by either image-based localization or a PDR are usually in a relative coordinate system. Therefore, this research proposes a system, which uses space resection-aided PDR with geo-referenced images of a previously mapped environment to enable seamless navigation and solve the shortcomings of PDR and image-based localization, and evaluates the performance of space resection with different assumptions using a smartphone. The indoor mobile mapping system (IMMS) is used for the effective production of geo-referenced images. The preliminary results indicate that the proposed algorithm is suitable for universal pedestrian indoor navigation, achieving the accuracy required for commercial applications. [ABSTRACT FROM AUTHOR]

In this paper, an integrated navigation system based on H-infinity filter is proposed for improving the performance of underwater navigation system. The proposed navigation system consists of a package with a triad of accelerometers and an Attitude and Heading Reference System (AHRS) as the main system and a Doppler Velocity Log (DVL) as an auxiliary system. In this system, the dynamic and measurement models are defined in such a way by which a H-infinity based integrated navigation system is derived. In addition, according to the specific dynamic of discussed vehicle, a scheme is proposed for estimating the performance bound parameter of H-infinity filter. The proposed system becomes robust versus the system modeling errors and noise uncertainty in comparison with the conventional Kalman Filter (KF). Furthermore, it can be effectively decrease the effect of the DVL's outlier data. The performance of the proposed system is evaluated through several sea tests using an Autonomous Underwater Vehicle (AUV). Furthermore, the conventional Dead Reckoning (DR) and KF algorithms are utilized for comparison. The experimental results demonstrate the superior performance of the proposed system compared with the conventional algorithms. [ABSTRACT FROM AUTHOR]

Location-based services (LBS) are services offered through a mobile device that take into account a device's geographical location. To provide position information for these services, location is a key process. GNSS (Global Navigation Satellite System) can provide sub-meter accuracy in open-sky areas using satellite signals. However, for indoor and dense urban environments, the accuracy deteriorates significantly because of weak signals and dense multipaths. The situation becomes worse in indoor environments where the GNSS signals are unreliable or totally blocked. To improve the accuracy of indoor positioning for location-based services, an improvedWiFi/Pedestrian Dead Reckoning (PDR) integrated positioning and navigation system using an adaptive and robust filter is presented. The adaptive filter is based on scenario and motion state recognition and the robust filter is based on the Mahalanobis distance. They are combined and used in the WiFi/PDR integrated system to weaken the effect of gross errors on the dynamic and observation models. To validate their performance in the WiFi/PDR integrated system, a real indoor localization experiment is conducted. The results indicate that the adaptive filter is better able to adapt to the circumstances of the dynamic model by adjusting the covariance of the process noise and the robust Kalman filter is able to mitigate the harmful effect of gross errors from the WiFi positioning. [ABSTRACT FROM AUTHOR]

Although map filtering-aided Pedestrian Dead Reckoning (PDR) is capable of largely improving indoor localization accuracy, it becomes less efficient when coping with highly complex indoor spaces. For instance, indoor spaces with a few close corners or neighboring passages can lead to particles entering erroneous passages, which can further cause the failure of subsequent tracking. To address this problem, we propose GridiLoc, a reliable and accurate pedestrian indoor localization method through the fusion of smartphone sensors and a grid model. The key novelty of GridiLoc is the utilization of a backtracking grid filter for improving localization accuracy and for handling dead ending issues. In order to reduce the time consumption of backtracking, a topological graph is introduced for representing candidate backtracking points, which are the expected locations at the starting time of the dead ending. Furthermore, when the dead ending is caused by the erroneous step length model of PDR, our solution can automatically calibrate the model by using the historical tracking data. Our experimental results show that GridiLoc achieves a higher localization accuracy and reliability compared with the commonly-used map filtering approach. Meanwhile, it maintains an acceptable computational complexity. [ABSTRACT FROM AUTHOR]

Indoor positioning has recently become an important field of interest because global navigation satellite systems (GNSS) are usually unavailable in indoor environments. Pedestrian dead reckoning (PDR) is a promising localization technique for indoor environments since it can be implemented on widely used smartphones equipped with low cost inertial sensors. However, the PDR localization severely suffers from the accumulation of positioning errors, and other external calibration sources should be used. In this paper, a context-recognition-aided PDR localization model is proposed to calibrate PDR. The context is detected by employing particular human actions or characteristic objects and it is matched to the context pre-stored offline in the database to get the pedestrian's location. The Hidden Markov Model (HMM) and Recursive Viterbi Algorithm are used to do the matching, which reduces the time complexity and saves the storage. In addition, the authors design the turn detection algorithm and take the context of corner as an example to illustrate and verify the proposed model. The experimental results show that the proposed localization method can fix the pedestrian's starting point quickly and improves the positioning accuracy of PDR by 40.56% at most with perfect stability and robustness at the same time. [ABSTRACT FROM AUTHOR]

The article describes a study of problem of estimating the position coordinates of Autonomous Biomimetic Underwater Vehicle (ABUV) using two methods: dead reckoning (DR) and extended Kalman filter (EKF). In the first part of the paper, navigation system of ABUV is described and scientific problem with underwater positioning is formulated. The main part describes a way of estimating the position coordinates using DR and EKF and a numerical experiment involving motion of ABUV along the predetermined test distance. The final part of the paper contains a comparative statistical analysis of the results, carried out for assessing the accuracy of estimation of the position coordinates using DR and EKF methods. It presents the generalized conclusions from the research and the problems relating to the proper placement of the components of the system measuring distances. [ABSTRACT FROM AUTHOR]