Indoor Pedestrian Research Articles

A Method for Autonomous Multi-Motion Modes Recognition and Navigation Optimization for Indoor Pedestrian.

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.

A Pedestrian Indoor Navigation System Using Deep-Learning-Aided Cellular Signals and ZUPT-Aided Foot-Mounted IMUs

A signal-sensor-based indoor pedestrian navigation system is developed. The proposed system: 1) utilizes a foot-mounted inertial navigation system (INS), in which the accumulated errors are mitigated via a zero velocity update (ZUPT) approach; 2) exploits opportunistically cellular long-term evolution (LTE) signals in a deep neural network (DNN)-based synthetic aperture navigation (SAN) framework, in which the pedestrian’s motion is utilized to suppress multipath-induced errors. The proposed DNN-SAN-LTE-ZUPT-INS (DUALS) indoor pedestrian navigation system utilizes the complementary the desirable characteristics of both subsystems, coupled via two architectures: (a) loosely-coupled and (b) tightly-coupled. This paper designs and assesses both architectures experimentally in an indoor environment. The experimental study demonstrates a pedestrian traversing a trajectory of 600 m in 14 minutes, including a stationary period, straight segments, up/down the stairs, and riding in an elevator, while receiving signals from 4 LTE base stations (also known as evolved node Bs (eNodeBs)). The proposed tightly-coupled DUALS system exhibited a three-dimensional (3-D) position root mean-squared error (RMSE) of 1.34 m, outperforming the loosely-coupled DUALS, ZUPT-aided INS, and LTE-DNN-SAN, which achieved a position RMSE of 1.38, 1.49, and 1.97 m, respectively.

Integration of Wearable Sensors Measurements for Indoor Pedestrian Tracking

Wearable sensors have great potential to ensure the safety and security of humans in hazardous and unknown environments. Real-time tracking and mapping are key aspects of safety and security. Integrating body-mounted wearable sensors measurements for human tracking is challenging due to the high degree of freedom of human motion. Particularly, time synchronization and spatial alignment between different sensors make the integration of measurements challenging. Also, weight, power, and computational resources impose additional constraints. This article addresses these challenges by demonstrating the design and implementation of a multisensor pedestrian tracking system that integrates, spatially aligns, and time synchronizes state-of-art wearable tracking and positioning sensors/technologies. The integrated measurements are packaged into labelled datasets that will be publicly available and easily accessible for researchers. The dataset includes raw and processed data of multiple wearable inertial sensors, a stereo camera, a handheld LiDAR, and an ultrawide-band (UWB) receiver. The dataset is supported by a high-accuracy ground truth reference generated by a post-processed 3D LiDAR SLAM engine. Assessment of state-of-art tracking technologies/sensors is covered, and details of tracking algorithms and their performance and challenges are demonstrated.

Adaptive Particle Filter with Abnormal Detection for Wearable Indoor Pedestrian Navigation

Currently, indoor location-based services (ILBSs) have increasing requirements in people’s daily life. In the meanwhile, the wearable devices are becoming more popular. In this paper, we studied a wearable system for indoor localization mainly based on INS/UWB. In order to achieve high-precision, stable, and continuous positioning, a sensor fusion method with anomaly detection is proposed. In the method, the sensor fusion method is derived from Bayesian estimation and a particle filter is developed to solve the nonlinearity problem and non-Gaussian errors for indoor positioning. In addition, the anomaly detection eliminates effects of NLoS and multipath effects significantly with the Mahalanobis distance. Two field experiments are conducted, and the results demonstrate that the 90% error of the proposed adaptive particle filter is 0.53 m, which is a 40% decrease compared with the PDR-only and UWB-only and classic PF, indicating better robustness and stability.

Magnetic Field-Enhanced Learning-Based Inertial Odometry for Indoor Pedestrian

Pedestrian dead-reckoning (PDR) is a vital technique in pedestrian localization. Compared with traditional PDR, learning-based inertial odometry has the advantages of smaller position drift and is insensitive to pedestrian motion patterns. However, the heading drift of the trajectory is still the dominant error source for the position error drift in these methods. This study focuses on providing a pedestrian trajectory estimation method with low drift by properly fusing learned-based inertial odometry and magnetometer measurements under an indoor scenario containing significant magnetic field disturbance. The proposed method reduces the impact of magnetic field disturbance by adopting a long-term average magnetic vector, which is far more stable than using local magnetic vectors. Meanwhile, the proposed method can estimate the magnetometer bias online rather than depending on precalibrated magnetometer measurements. The test results show that the proposed method can obtain superior positioning performance using uncalibrated raw magnetometer data compared to other methods, even using calibrated magnetometer data. Simultaneously, this method achieves a balance between algorithm accuracy and efficiency.

An Efficient and Low-Latency Deep Inertial Odometer for Smartphone Positioning

The Global Positioning System (GPS) is not an effective solution for pedestrian indoor navigation using embedded devices because of poor signal penetration and high power requirements. Indoor positioning based purely on commercial-grade inertial measurement units (IMUs) may provide a good alternative but their significant noise and random bias must be eliminated. Although Kalman filters and pedestrian dead reckoning helped reduce the errors using IMUs, these methods cannot prevent the divergence of estimated position error. Deep learning was proposed for accurate position estimation with IMUs. Despite the progress, robust position estimation in mobile embedded devices using deep learning has not been established yet because of memory requirements, latency and inaccurate position estimation especially for stationary pedestrians. In this study, we present an efficient embedded deep learning approach for robust, real-time and accurate pedestrian position estimation using commercial Android smartphone IMUs. We first extended a publicly available deep inertial navigation dataset (OxIOD) with stationary data to enhance the positioning accuracy for both steady state and motion. Next, we trained and tested a deep learning architecture that yields a higher positioning accuracy with 50% lower network latency and 31% lower network size compared with earlier deep positioning networks such as IONet. Our real-time tests of the model in an Android smartphone indicated that the extension for the dataset reduces the position shift when the smartphone is stationary. Since our embedded deep learning solution simultaneously decreases the positioning error, latency and memory requirements, the solution paves the way for numerous practical indoor navigation applications.

Smartphone-Based Pedestrian Dead Reckoning for 3D Indoor Positioning.

Indoor localization based on pedestrian dead reckoning (PDR) is drawing more and more attention of researchers in location-based services (LBS). The demand for indoor localization has grown rapidly using a smartphone. This paper proposes a 3D indoor positioning method based on the micro-electro-mechanical systems (MEMS) sensors of the smartphone. A quaternion-based robust adaptive cubature Kalman filter (RACKF) algorithm is proposed to estimate the heading of pedestrians based on magnetic, angular rate, and gravity (MARG) sensors. Then, the pedestrian behavior patterns are distinguished by detecting the changes of pitch angle, total accelerometer and barometer values of the smartphone in the duration of effective step frequency. According to the geometric information of the building stairs, the step length of pedestrians and the height difference of each step can be obtained when pedestrians go up and downstairs. Combined with the differential barometric altimetry method, the optimal height can be computed by the robust adaptive Kalman filter (RAKF) algorithm. Moreover, the heading and step length of each step are optimized by the Kalman filter to reduce positioning error. In addition, based on the indoor map vector information, this paper proposes a heading calculation strategy of the 16-wind rose map to improve the pedestrian positioning accuracy and reduce the accumulation error. Pedestrian plane coordinates can be solved based on the Pedestrian Dead-Reckoning (PDR). Finally, combining pedestrian plane coordinates and height, the three-dimensional positioning coordinates of indoor pedestrians are obtained. The proposed algorithm is verified by actual measurement examples. The experimental verification was carried out in a multi-story indoor environment. The results show that the Root Mean Squared Error (RMSE) of location errors is 1.04–1.65 m by using the proposed algorithm for three participants. Furthermore, the RMSE of height estimation errors is 0.17–0.27 m for three participants, which meets the demand of personal intelligent user terminal for location service. Moreover, the height parameter enables users to perceive the floor information.

Review Pemetaan Domain Pengetahuan Tentang Navigasi Dalam Ruangan Melalui Pendekatan Bibliomatrik

Indoor navigation has been recognized as a technological innovation in the field of navigation. Due to the limitations of GPS in indoor areas, indoor navigation has become a growing area of research in recent years. Due to the rapid development of research in the field of indoor navigation, various stakeholders require an up-to-date review of the research and implementation of indoor navigation. The purpose of this study is to provide an objective and accurate summary of the domain in indoor navigation research using 1618 papers related to indoor navigation taken from the SCOPUS database from 2016 to 2021 using bibliometric analysis. The results are useful for identifying clusters and research topics in this community, such as mobile robots, indoor navigation systems, 3d pedestrian indoor navigation and other clusters. More importantly, these results can help highlight how research related to indoor navigation has evolved, thereby greatly contributing to understanding the underlying structure of the field. This study offers useful and new insights to encapsulate the status quo of Indoor Navigation knowledge and can be used as a dynamic platform to integrate the future development of Indoor Navigation

Applying a ToF/IMU-Based Multi-Sensor Fusion Architecture in Pedestrian Indoor Navigation Methods.

The advancement of indoor Inertial Navigation Systems (INS) based on the low-cost Inertial Measurement Units (IMU) has been long reviewed in the field of pedestrian localization. There are various sources of error in these systems which lead to unstable and unreliable positioning results, especially in long term performances. These inaccuracies are usually caused by imperfect system modeling, inappropriate sensor fusion models, heading drift, biases of IMUs, and calibration methods. This article addresses the issues surrounding unreliability of the low-cost Micro-Electro-Mechanical System (MEMS)-based pedestrian INS. We designed a novel multi-sensor fusion method based on a Time of Flight (ToF) distance sensor and dual chest- and foot-mounted IMUs, aided by an online calibration technique. An Extended Kalman Filter (EKF) is accounted for estimating the attitude, position, and velocity errors, as well as estimation of IMU biases. A fusion architecture is derived to provide a consistent velocity measurement by operative contribution of ToF distance sensor and foot mounted IMU. In this method, the measurements of the ToF distance sensor are used for the time-steps in which the Zero Velocity Update (ZUPT) measurements are not active. In parallel, the chest mounted IMU is accounted for attitude estimation of the pedestrian’s chest. As well, by designing a novel corridor detection filter, the heading drift is restricted in each straightway. Compared to the common INS method, developed system proves promising and resilient results in two-dimensional corridor spaces for durations of up to 11 min. Finally, the results of our experiments showed the position RMS error of less than 3 m and final-point error of less than 5 m.

Time- and Resource-Efficient Time-to-Collision Forecasting for Indoor Pedestrian Obstacles Avoidance.

As difficult vision-based tasks like object detection and monocular depth estimation are making their way in real-time applications and as more light weighted solutions for autonomous vehicles navigation systems are emerging, obstacle detection and collision prediction are two very challenging tasks for small embedded devices like drones. We propose a novel light weighted and time-efficient vision-based solution to predict Time-to-Collision from a monocular video camera embedded in a smartglasses device as a module of a navigation system for visually impaired pedestrians. It consists of two modules: a static data extractor made of a convolutional neural network to predict the obstacle position and distance and a dynamic data extractor that stacks the obstacle data from multiple frames and predicts the Time-to-Collision with a simple fully connected neural network. This paper focuses on the Time-to-Collision network’s ability to adapt to new sceneries with different types of obstacles with supervised learning.

Robust and Accurate Step Counting Based on Motion Mode Recognition for Pedestrian Indoor Positioning Using a Smartphone

Robust and accurate step counting plays an essential role in some fields, such as indoor positing, behavior recognition, and health management. Currently, there are numerous applications or methods in step counting. However, most solutions did not emphasize the elimination of false steps in the non-walking state, which still encounters the over-counting or under-counting problem. In this study, a robust and accurate step counting solution based on movement mode recognition is proposed. First, according to the characteristics of pedestrians walking, the SVM and FSM-DT classifiers were constructed to recognize the user’s motion state and phone usage mode, respectively. The purpose of classification is to solve the step-counting error of non-walking state and initialize a suitable parameter for different cases. Then, we utilized a crest-valley detection algorithm with multi-feature constrained to detect the step, and the initial threshold values for each mode can be adjusted adaptively during walking. The results indicate that the accuracy of the proposed method can reach 99.11% and 97.43% in normal and free walking. Compared with standard peak detection and an excellent method, the proposed method can improve by 44.14% and 18.71% for false walking, respectively, which is a significant improvement in the robustness and accuracy. Furthermore, we also achieve an average accuracy higher than 98% in some typically carrying modes and ways, and higher accuracy compared with four popular step-counting mobile applications in different walking states.

An Adaptive Sensor Fusion Framework for Pedestrian Indoor Navigation in Dynamic Environments

Indoor navigation is a representative application of an indoor positioning system that uses a variety of equipment, including smartphones with various sensors. Many indoor navigation systems utilize Wi-Fi signals, as well as a variety of inertial sensors, such as a 3D accelerometer, digital compass, gyroscope, and barometer, to improve the accuracy of user location tracking. The inertial sensors are vulnerable to changes in the surrounding environments and sensitive to users behavior, but little research has been conducted on sensor fusion under these conditions. In this paper, we propose a dynamic sensor fusion framework (DSFF) that provides accurate user tracking results by dynamically calibrating inertial sensor readings in a sensor fusion process. The proposed method continually learns the errors and biases of each sensor due to the changes in user behavior patterns and surrounding environments. The learned patterns are then dynamically applied to the user tracking process to yield accurate results. The results of experiments conducted in both a single-story and a multi-story building confirm that DSFF provides accurate tracking results. The scalability of the DSFF will enable it to provide more accurate tracking results with various sensors, both existing and under development.

Lower Limb Model Based Inertial Indoor Pedestrian Navigation System for Walking and Running

Foot-mounted inertial navigation systems are suitable in GNSS denied environment for special tasks such as fire rescue, soldier, etc. These tasks are often accompanied by vigorous movements such as running and jumping, which is a challenge for the Zero Velocity Update (ZUPT) based inertial navigation system. As the speed increases, the inertial sensor drift increases, while the zero-velocity interval shortens, making the gait detection more difficult and resulting in a limited error correction. And as the motion intensifies, the stability of the system decreases. A multi-node inertial pedestrian navigation system based on the lower limb model constraint is proposed in this paper. Our first contribution in this paper is studying a simplified adaptive zero-bias estimation algorithm. The changes in velocity and angular velocity between two steps are used to estimate and compensate for the drift of accelerometers and gyroscopes. Our second contribution is proposing a novel gait segmentation method by lower limb motion statistics feature. A hybrid detection model of dual IMU is conducted to improve the accuracy of gait recognition. After that, the maximum distance inequality constraint is studied to correct the position error when the still phase is not available in vigorous movement. The final contribution is studying the chi-square fault detection method to improve the stability of KF. Multiple experiments prove that the proposed multi-node inertial pedestrian navigation system is effective in dealing with various challenging in walking, running, and hybrid motion pedestrian inertial navigation problems which could achieve less than 0.3% average distance error.

An integrity monitoring algorithm for WiFi/PDR/smartphone-integrated indoor positioning system based on unscented Kalman filter

Indoor positioning navigation technologies have developed rapidly, but little effort has been expended on integrity monitoring in Pedestrian Dead Reckoning (PDR) and WiFi indoor positioning navigation systems. PDR accuracy will drift over time. Meanwhile, WiFi positioning accuracy decreases in complex indoor environments due to severe multipath propagation and interference with signals when people move about. In our research, we aimed to improve positioning quality with an integrity monitoring algorithm for a WiFi/PDR-integrated indoor positioning system based on the unscented Kalman filter (UKF). The integrity monitoring is divided into three phases. A test statistic based on the innovation of UKF determines whether the positioning system is abnormal. Once a positioning system abnormality is detected, a robust UKF (RUKF) is triggered to achieve higher positioning accuracy. Again, the innovation of RUKF is used to judge the outliers in observations and identify positioning system faults. In the last integrity monitoring phase, users will be alerted in time to reduce the risk from positioning fault. We conducted a simulation to analyze the computational complexity of integrity monitoring. The results showed that it did not substantially increase the overall computational complexity when the number of dimensions in the state vector and observation vector in the system is small (< 20). In practice, the number of dimensions of state vector and observation vector in an indoor positioning system rarely exceeds 20. The proposed integrity monitoring algorithm was tested in two field experiments, showing that the proposed algorithm is quite robust, yielding higher positioning accuracy than the traditional method, using only UKF.

UWB/INS Integrated Pedestrian Positioning for Robust Indoor Environments

Goal: Accurate robust ultra wideband (UWB) pedestrian indoor positioning becomes a challenging task when faced with robust indoor environments such as non-line of sight (NLOS) due to refraction of signals, multipath effect, etc. When the UWB system is faced with such a robust environment, its positioning accuracy is difficult to reach the centimeter level and the reliability of the system operation needs to be improved. In order to solve such problems, an ultra wideband/inertial navigation system (UWB/INS) integrated pedestrian navigation algorithm is proposed. On the one hand, UWB-based joint state estimation particle filtering is used for position calculation, and on the other hand, INS-based zero-speed update (ZUPT) algorithm is used for navigation information solution. Under the framework of the INS error equation, the navigation information fusion of the two systems is carried out. In the simple pedestrian environment, the average positioning accuracy of the UWB/INS algorithm is 53.8% higher than that of the UWB algorithm, 40% higher than the INS algorithm and 31% higher than Original UWB/INS algorithm. Under the robust pedestrian indoor positioning, the average positioning accuracy of the UWB/INS algorithm is 39.7% higher than that of the UWB algorithm, 37.5% higher than the INS algorithm and 53% higher than Original UWB/INS algorithm. The results of two sets of pedestrian indoor positioning experiments demonstrate the effectiveness of our approach.

Emergency Positioning Method of Indoor Pedestrian in Non-Cooperative Navigation Environment Based on Virtual Reference Node Array/INS

Aiming at the requirements of positioning under emergency situations such as terrorist activities, natural disasters, etc., we propose an emergency positioning method of indoor pedestrian in non-cooperative navigation environment based on virtual reference node array/inertial navigation system in this paper. This method bases only on a reference node that has been placed indoor temporarily, and uses the distance information between the single reference node and the move node carried by pedestrian. In this emergency positioning method, we establish the virtual reference node array and virtual ranging overdetermined equations to obtain the pedestrian’s three-dimensional positioning coordinates without accumulated error by using the surface intersection physical model and least square method. Furthermore, we combine the positioning results of inertial navigation and wireless virtual reference node array method, so that the fluctuation of wireless positioning error is suppressed, and long-term and high-precision positioning is achieved. Finally, we choose LinkTrack’s UWB local positioning result as the reference comparison standard, and conduct experimental verification and performance evaluation of the proposed method. The experimental result shows that the average error of the proposed positioning method along X-axis and Y-axis are 0.049m and 0.056m respectively, which indicates that this method has similar positioning accuracy with LinkTrack’s UWB local positioning system, and it can realize the accurate positioning and tracking of indoor pedestrian in non-cooperation navigation environment.

Beyond Euclidean Distance for Error Measurement in Pedestrian Indoor Location

Indoor positioning systems (IPSs) suffer from a lack of standard evaluation procedures enabling credible comparisons: this is one of the main challenges hindering their widespread market adoption. Traditionally, accuracy evaluation is based on positioning errors defined as the Euclidean distance between the true positions and the estimated positions. While Euclidean is simple, it ignores obstacles and floor transitions. In this article, we describe procedures that measure a positioning error defined as the length of the pedestrian path that connects the estimated position to the true position. The procedures apply pathfinding on floor maps using visibility graphs (VGs) or navigational meshes (NMs) for vector maps and fast marching (FM) for raster maps. Multifloor and multibuilding paths use the information on vertical in-building communication ways and outdoor paths. The proposed measurement procedures are applied to position estimates provided by the IPSs that participated in the EvAAL-ETRI 2015 competition. Procedures are compared in terms of pedestrian path realism, indoor model complexity, path computation time, and error magnitudes. The VGs algorithm computes shortest distance paths; NMs produce very similar paths with significantly shorter computation time; and FM computes longer, more natural-looking paths at the expense of longer computation time and memory size. The 75th percentile of the measured error differs among the methods from 2.2 to 3.7 m across the evaluation sets.

Improved Position Accuracy of Foot-Mounted Inertial Sensor by Discrete Corrections from Vision-Based Fiducial Marker Tracking.

In this paper, we present a novel pedestrian indoor positioning system that uses sensor fusion between a foot-mounted inertial measurement unit (IMU) and a vision-based fiducial marker tracking system. The goal is to provide an after-action review for first responders during training exercises. The main contribution of this work comes from the observation that different walking types (e.g., forward walking, sideways walking, backward walking) lead to different levels of position and heading error. Our approach takes this into account when accumulating the error, thereby leading to more-accurate estimations. Through experimentation, we show the variation in error accumulation and the improvement in accuracy alter when and how often to activate the camera tracking system, leading to better balance between accuracy and power consumption overall. The IMU and vision-based systems are loosely coupled using an extended Kalman filter (EKF) to ensure accurate and unobstructed positioning computation. The motion model of the EKF is derived from the foot-mounted IMU data and the measurement model from the vision system. Existing indoor positioning systems for training exercises require extensive active infrastructure installation, which is not viable for exercises taking place in a remote area. With the use of passive infrastructure (i.e., fiducial markers), the positioning system can accurately track user position over a longer duration of time and can be easily integrated into the environment. We evaluated our system on an indoor trajectory of 250 m. Results show that even with discrete corrections, near a meter level of accuracy can be achieved. Our proposed system attains the positioning accuracy of 0.55 m for a forward walk, 1.05 m for a backward walk, and 1.68 m for a sideways walk with a 90% confidence level.

Simultaneous Indoor Pedestrian Localization and House Mapping Based on Inertial Measurement Unit and Bluetooth Low-Energy Beacon Data.

Indoor location estimation is crucial to provide context-based assistance in home environments. In this study, a method for simultaneous indoor pedestrian localization and house mapping is proposed and evaluated. The method fuses a person’s movement data from an Inertial Measurement Unit (IMU) with proximity and activity-related data from Bluetooth Low-Energy (BLE) beacons deployed in the indoor environment. The person’s and beacons’ localization is performed simultaneously using a combination of particle and Kalman Filters. We evaluated the method using data from eight participants who performed different activities in an indoor environment. As a result, the average participant’s localization error was 1.05 ± 0.44 m, and the average beacons’ localization error was 0.82 ± 0.24 m. The proposed method is able to construct a map of the indoor environment by localizing the BLE beacons and simultaneously locating the person. The results obtained demonstrate that the proposed method could point to a promising roadmap towards the development of simultaneous localization and home mapping system based only on one IMU and a few BLE beacons. To the best of our knowledge, this is the first method that includes the beacons’ data movement as activity-related events in a method for pedestrian Simultaneous Localization and Mapping (SLAM).

A Novel Position and Orientation System for Pedestrian Indoor Mobile Mapping System

A reliable and accurate position and orientation system (POS) is essential for indoor mobile mapping system (MMS). We propose a novel pedestrian POS solution for indoor MMS using backpack INS, foot mounted pedestrian dead reckoning (Foot-PDR) and sparse control points with known coordinates. The Foot-PDR can provide continuous high precision positioning with the corrections of the control points to maintain its absolute accuracy. Such precise positioning is integrated with the backpack INS to estimate the accurate position and orientation, similar to the GNSS/INS integration for the outdoor environment. The lever-arm variation issue between the backpack INS and the Foot-PDR is solved by catching a specific phase of the gait cycle that has consistent relative pose between the back and the heel of pedestrians. The feasibility of the proposed POS was verified by an outdoor large-scale open-sky area and an indoor office environment tests. The outdoor experimental results demonstrate that the estimated horizontal position and yaw angle has an accuracy of 0.07 m and 0.6°, respectively (when the control points are as sparse as 50 meters). The indoor experimental results demonstrate that the position accuracy is 0.07 m, 0.08 m, and 0.04 m, along the north, east, and height directions. Furthermore, the proposed POS does not require expensive infrastructure support and is immune to indoor environment changes.