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The indoor navigation method shows great application prospects that is based on a wearable foot-mounted inertial measurement unit and a zero-velocity update principle. Traditional navigation methods mainly support two-dimensional stable motion modes such as walking; special tasks such as rescue and disaster relief, medical search and rescue, in addition to normal walking, are usually accompanied by running, going upstairs, going downstairs and other motion modes, which will greatly affect the dynamic performance of the traditional zero-velocity update algorithm. Based on a wearable multi-node inertial sensor network, this paper presents a method of multi-motion modes recognition for indoor pedestrians based on gait segmentation and a long short-term memory artificial neural network, which improves the accuracy of multi-motion modes recognition. In view of the short effective interval of zero-velocity updates in motion modes with fast speeds such as running, different zero-velocity update detection algorithms and integrated navigation methods based on change of waist/foot headings are designed. The experimental results show that the overall recognition rate of the proposed method is 96.77%, and the navigation error is 1.26% of the total distance of the proposed method, which has good application prospects. [ABSTRACT FROM AUTHOR]

Foot-mounted positioning devices are becoming more and more popular in the different application field. For example, inertial sensors are now embedded in safety shoes to monitor security. They allow positioning with zero velocity update to bound the error growth of foot-mounted inertial sensors. High positioning accuracy depends on robust zero velocity detector (ZVD). Existing Artificial Intelligent (AI)-based methods classify the pedestrian dynamics to adjust ZVD at the cost of high computation costs and error propagation from miss-classification. We propose a machine learning model to detect zero velocity moments without any pre-classification step, named Uniform AI Model for All pedestrian Motions (UMAM). Performance is evaluated by benchmarking on two new subjects of opposite gender and different size, not included in the training data set, over complex indoor/outdoor paths of 2 km for subject 1 and 2.1 km for subject 2. We obtain an average 2D loop closure error of less than 0.37%. [ABSTRACT FROM AUTHOR]

Robust and accurate zero-velocity detection can improve the performance of zero-velocity-aided foot-mounted inertial navigation system. To ensure the accuracy of zero-velocity detection, we propose a novel detector based on contrastive learning. This detector roughly eliminates the inertial data that must not be the zero-velocity event in advance, to reduce the computation cost. Then the detector uses the remaining inertial data to detect the zero-velocity event via a trained contrastive neural network. The contrastive neural network uses the triplet network and is trained by comparing with the anchor data which consists of the known static inertial data from the period of initial alignment. The classifier will finally determine whether the output of the triplet network is the zero-velocity event. Two experiments were conducted to evaluate this novel detector, showing that it can adaptively and accurately detect the zero-velocity event. The horizontal position errors of the two experiments are respectively 1.33m over a 953m outdoor path with walking and 3.74m over a 1143m indoor/outdoor path with combined motion of low dynamic and high dynamic. [ABSTRACT FROM AUTHOR]

In this article, we develop a pedestrian inertial navigation (PIN) that uses an inertial measurement unit (IMU) mounted on a shoe to locate a pedestrian in an infrastructure-free environment. In the PIN, stand-alone navigation is possible by calculating the navigation solution using the inertial navigation system (INS) mechanism. For accurate navigation, however, it is necessary to compensate for accumulated errors over time. In a foot-mounted PIN, the error can be compensated through zero-velocity (ZV) correction method by detecting the moment the foot touches the ground. One important point here is to detect ZVs accurately. In most of the literature related to this, ZV detection techniques for normal gait have been proposed. In this article, we first propose a powerful ZV detection algorithm for various gait types. This algorithm consists of a new buffering method of the sensor output power, an exact ZV detection method through glitch removal, and a reinforcement method for ZV detection based on auxiliary step detection. Second, we design a PIN/ZV integration filter, and describe the real-time operation plan of all these functions. The performance of the PIN using the proposed algorithm is verified through experiments. [ABSTRACT FROM AUTHOR]

A GNSS/INS integrated navigation system has been intensively developed and widely applied in multiple areas. It can provide high accuracy position, velocity and attitude for vehicle with appropriate data fusion algorithm. However, the overall performance of a low-cost GNSS/MEMS IMU frequently degrades in shaded environment. The traditional constraints GNSS/MIMU algorithm based on zero-velocity detection can effectively increase positioning performance, but easily be susceptible to false detection. This article aims to improve a ZUPT/DZUPT constraints model to improve the accuracy of navigation solutions during satellites signal blockages for different motion states. Firstly, we present a tightly coupled strategy to integrate GPS/BDS and INS by applying EKF. Then, a compositive static zero-velocity detection scheme is carried out by using the Vondrak low pass filter, GNSS/INS calculated velocity and the original data of INS. Meanwhile, a dynamic ZUPT constraint model is also constructed based on the motion characteristics of vehicle. An vehicle test was performed to validate the new algorithm. The results indicate that proposed method can effectively improve the success rate of zero-velocity detection. When the satellite signal is interrupted for 120 s, the position and velocity accuracy of the vehicle are improved by 74.7%~ 96% and 47%~ 86.2% respectively. [ABSTRACT FROM AUTHOR]

The indoor navigation method shows great application prospects that is based on a wearable foot-mounted inertial measurement unit and a zero-velocity update principle. Traditional navigation methods mainly support two-dimensional stable motion modes such as walking; special tasks such as rescue and disaster relief, medical search and rescue, in addition to normal walking, are usually accompanied by running, going upstairs, going downstairs and other motion modes, which will greatly affect the dynamic performance of the traditional zero-velocity update algorithm. Based on a wearable multi-node inertial sensor network, this paper presents a method of multi-motion modes recognition for indoor pedestrians based on gait segmentation and a long short-term memory artificial neural network, which improves the accuracy of multi-motion modes recognition. In view of the short effective interval of zero-velocity updates in motion modes with fast speeds such as running, different zero-velocity update detection algorithms and integrated navigation methods based on change of waist/foot headings are designed. The experimental results show that the overall recognition rate of the proposed method is 96.77%, and the navigation error is 1.26% of the total distance of the proposed method, which has good application prospects.

Step detection is very important for the foot-mounted Pedestrian Dead Reckoning (PDR) system. To deal with the false detection caused by variant stepping modes and occasional disturbance, a novel robust step detection algorithm is proposed. The proposed algorithm detects the number of steps and zero-velocity phase using data from inertial sensors mounted on the user’s foot. It includes two parts: a novel anti-fault detection algorithm and a novel zero-velocity detection method, which are used to reduce the over-detection and miss-detection of steps, respectively. Field tests show that the proposed approach is robust under varying motion speeds in different step modes for different persons and the average accuracy of the proposed algorithm is 99.94% in all motion modes, which significantly exceeds conventional estimation approaches in terms of step detection accuracy. [ABSTRACT FROM AUTHOR]

Zero velocity update is a common and efficient approach to bound the accumulated error growth for foot-mounted inertial navigation system. Thus a robust zero velocity detector (ZVD) for all kinds of locomotion is needed for high accuracy pedestrian navigation systems. In this paper, we investigate two machine learning-based ZVDs: Histogram-based Gradient Boosting (HGB) and Random Forest (RF), aiming at adapting to different motion types while reducing the computation costs compared to the deep learning-based detectors. A complete data pre-processing procedure, including a feature engineering study and data augmentation techniques, is proposed. A motion classifier based on HGB is used to distinguish “single support” and “double float” motions. This concept is different from the traditional locomotion classification (walking, running, stair climbing) since it merges similar motions into the same class. The proposed ZVDs are evaluated with inertial data collected by two subjects over a 1.8 km indoor/outdoor path with different motions and speeds. The results show that without huge training dataset, these two machine learning-based ZVDs achieve better performances (55 cm positioning accuracy) and lower computational costs than the two deep learning-based Long Short-Term Memory methods (1.21 m positioning accuracy). [ABSTRACT FROM AUTHOR]

We present two novel techniques for detecting zero-velocity events to improve foot-mounted inertial navigation. Our first technique augments a classical zero-velocity detector by incorporating a motion classifier that adaptively updates the detector’s threshold parameter. Our second technique uses a long short-term memory (LSTM) recurrent neural network to classify zero-velocity events from raw inertial data, in contrast to the majority of zero-velocity detection methods that rely on basic statistical hypothesis testing. We demonstrate that both of our proposed detectors achieve higher accuracies than existing detectors for trajectories including walking, running, and stair-climbing motions. Additionally, we present a straightforward data augmentation method that is able to extend the LSTM-based model to different inertial sensors without the need to collect new training data. [ABSTRACT FROM AUTHOR]

Pedestrian Navigation System (PNS) is one of the research focuses of indoor positioning in GNSS-denied environments based on the MEMS Inertial Measurement Unit (MIMU). However, in the foot-mounted pedestrian navigation system with MIMU or mobile phone as the main carrier, it is difficult to make the sampling time of gyros and accelerometers completely synchronous. The gyro-accelerometer asynchronous time affects the positioning of PNS. To solve this problem, a new error model of gyro-accelerometer asynchronous time is built. The effect of gyro-accelerometer asynchronous time on pedestrian navigation is analyzed. A filtering model is designed to calibrate the gyro-accelerometer asynchronous time, and a zero-velocity detection method based on the rate of attitude change is proposed. The indoor experiment shows that the gyro-accelerometer asynchronous time is estimated effectively, and the positioning accuracy of PNS is improved by the proposed method after compensating for the errors caused by gyro-accelerometer asynchronous time.

In this paper, a scheme is presented for fusing a foot-mounted Inertial Measurement Unit (IMU) and a floor map to provide ubiquitous positioning in a number of settings, such as in a supermarket as a shopping guide, in a fire emergency service for navigation, or with a hospital patient to be tracked. First, several Zero-Velocity Detection (ZDET) algorithms are compared and discussed when used in the static detection of a pedestrian. By introducing information on the Zero Velocity of the pedestrian, fused with a magnetometer measurement, an improved Pedestrian Dead Reckoning (PDR) model is developed to constrain the accumulating errors associated with the PDR positioning. Second, a Correlation Matching Algorithm based on map projection (CMAP) is presented, and a zone division of a floor map is demonstrated for fusion of the PDR algorithm. Finally, in order to use the dynamic characteristics of a pedestrian’s trajectory, the Adaptive Unscented Kalman Filter (A-UKF) is applied to tightly integrate the IMU, magnetometers and floor map for ubiquitous positioning. The results of a field experiment performed on the fourth floor of the School of Environmental Science and Spatial Informatics (SESSI) building on the China University of Mining and Technology (CUMT) campus confirm that the proposed scheme can reliably achieve meter-level positioning.

MEMS-SINS (Micro-Electro-Mechanical System – Strapdown Inertial navigation System) is always used for pedestrian navigation. But MEMS-SINS cannot fulfill navigation task independently for its low precision gyros and accelerometers, thus ZUPT (Zero-Velocity-Updating) should be introduced. ZUPT uses zeros-velocity when the foot contacting the ground as observation information aiding MEMS-SINS. Zero-velocity detection is a key and difficult technology. Currently, zero-velocity detection methods use the output of accelerometers or (and) gyros from MEMS-IMU (Inertial Measurement Unit) as measurement and compare those measurement with given threshold to judge zero-velocity constraints occur or not. Due to the diversity of pedestrian gaits and low signal-to-noise ratio of MEMS-IMU data, zero-velocity detection methods based on threshold detection own inherent shortcomings such as poor environmental adaptability and inconvenient usage. To overcome these, in this paper ideas using threshold detection are discarded, characteristics of pedestrians gaits is detailedly analyzed and two characters – zero-velocity interval must be located at the valley bottom of the generalized likelihood ratio statistical curves but the bottom of the curve is not necessarily zero-velocity interval – are concluded, thus a novel detecting method is designed. With proposed method, transformations including translation and symmetrical flip on generalized likelihood ratio statistical curves of MEMS-IMU are data are used to detect true zero-velocity interval and discard interfering curve bottoms. Theoretical analysis and experimental results show that the proposed method can detect zero-velocity occurring effectively without any given threshold and detecting rate on different groups of people and different velocity is 100%. [ABSTRACT FROM AUTHOR]

Pedestrian Navigation System (PNS) is one of the research focuses of indoor positioning in GNSS-denied environments based on the MEMS Inertial Measurement Unit (MIMU). However, in the foot-mounted pedestrian navigation system with MIMU or mobile phone as the main carrier, it is difficult to make the sampling time of gyros and accelerometers completely synchronous. The gyro-accelerometer asynchronous time affects the positioning of PNS. To solve this problem, a new error model of gyro-accelerometer asynchronous time is built. The effect of gyro-accelerometer asynchronous time on pedestrian navigation is analyzed. A filtering model is designed to calibrate the gyro-accelerometer asynchronous time, and a zero-velocity detection method based on the rate of attitude change is proposed. The indoor experiment shows that the gyro-accelerometer asynchronous time is estimated effectively, and the positioning accuracy of PNS is improved by the proposed method after compensating for the errors caused by gyro-accelerometer asynchronous time. [ABSTRACT FROM AUTHOR]

Pedestrian Navigation System (PNS) is one of the research focuses of indoor positioning in GNSS-denied environments based on the MEMS Inertial Measurement Unit (MIMU). However, in the foot-mounted pedestrian navigation system with MIMU or mobile phone as the main carrier, it is difficult to make the sampling time of gyros and accelerometers completely synchronous. The gyro-accelerometer asynchronous time affects the positioning of PNS. To solve this problem, a new error model of gyro-accelerometer asynchronous time is built. The effect of gyro-accelerometer asynchronous time on pedestrian navigation is analyzed. A filtering model is designed to calibrate the gyro-accelerometer asynchronous time, and a zero-velocity detection method based on the rate of attitude change is proposed. The indoor experiment shows that the gyro-accelerometer asynchronous time is estimated effectively, and the positioning accuracy of PNS is improved by the proposed method after compensating for the errors caused by gyro-accelerometer asynchronous time.