Indoor Pedestrian Dead Reckoning Research Articles

Indoor positioning method for pedestrian dead reckoning based on multi-source sensors

To solve the problems of severe error accumulation and low accuracy of pedestrian trajectory estimation in traditional Pedestrian Dead Reckoning (PDR) positioning technology, this paper proposes a multi-sensor fusion indoor PDR algorithm. Firstly, a generalized likelihood ratio multi-threshold detection algorithm is employed to detect the gait of pedestrians. Then, a linear multi-source information fusion model is constructed for step length estimation. Next, the quaternion strap-down attitude solution is utilized and coupled with an improved particle filter-unscented Kalman filter algorithm to correct heading angle deviations. Finally, integrate them into the PDR algorithm to estimate the pedestrian's position. The proposed PDR method's relative positioning errors for indoor two-dimensional plane and three-dimensional space walking are 0.36 % and 0.435 %, respectively. Compared to four traditional positioning algorithms, it reduces errors by approximately 0.77 %∼1.18 % and 5.42 %∼11.69 %, respectively. Experimental results indicate that the proposed PDR method effective suppression of error accumulation, achieving more accurate indoor PDR results.

Construction of features database using different platform navigation solutions for aiding PDR algorithm

Nowadays, navigation can be accomplished based on several technologies. Some of these technologies rely solely on camera vision-based algorithms, whereas others rely solely on motion sensors’ data. Furthermore, some solutions combine vision-based algorithms with data from motion sensors. In this study, three platform navigation algorithms (stereo visual odometry technique, combining the distance calculated from the visual optical flow with estimated heading from gyroscope sensor measurements, and combining the distance calculated from the visual optical flow with estimated heading from magnetometer sensor measurements) were compared to select the most accurate among them to be employed for features database construction. This features database, which contains various well-described features with known three-dimensional world coordinates, can be utilized to help any indoor pedestrian dead reckoning (PDR) navigation solution as well as mapping purposes. Tests' results demonstrated that vision-based technology (stereo visual odometry technique) is more accurate than the others however it should be aided with additional constraints to improve navigation positional accuracy. Afterward, this chosen technology was used to construct a well-described features database which was checked to assess its accuracy.

GPS-assisted Indoor Pedestrian Dead Reckoning

Indoor pedestrian dead reckoning (PDR) using embedded inertial sensors in smartphones has been actively studied in the ubicomp community. However, PDR relying only on inertial sensors suffers from the accumulation of errors from the sensors. Researchers have employed various indoor landmarks detectable by smartphone sensors such as magnetic fingerprints caused by elevators and Bluetooth signals from beacons with known coordinates to compensate for the errors. This study proposes a new type of indoor landmark that does not require additional device installation, e.g., beacons, and training data collection in a target environment, e.g., magnetic fingerprints, unlike existing landmarks. This study proposes the use of GPS signals received by a smartphone to correct the accumulated errors of the PDR. While it is impossible to locate the smartphone indoors using GPS satellites, the smartphone can receive signals at a window-side area through windows from satellites aligned with the orientation of the window normal. Based on this idea, we design a machine-learning-based module for detecting the proximity of a user to a window and the orientation of the window, which enables us to roughly determine the absolute coordinates of the smartphone and to correct the accumulated errors by referring to positions of window-side areas found in the floor plan of the environment. A key technical contribution of this study is designing the module, such that it can be trained based on data from environments other than the target environment yet work in any environment by extracting GPS-related information independent of wall orientation. We evaluated the effectiveness of the proposed method using sensor data collected in real environments.

Indoor PDR Positioning Assisted by Acoustic Source Localization, and Pedestrian Movement Behavior Recognition, Using a Dual‐Microphone Smartphone

In recent years, the public’s demand for location services has increased significantly. As outdoor positioning has matured, indoor positioning has become a focus area for researchers. Various indoor positioning methods have emerged. Pedestrian dead reckoning (PDR) has become a research hotspot since it does not require a positioning infrastructure. An integral equation is used in PDR positioning; thus, errors accumulate during long‐term operation. To eliminate the accumulated errors in PDR localisation, this paper proposes a PDR localisation system applied to complex scenarios with multiple buildings and large areas. The system is based on the pedestrian movement behavior recognition algorithm proposed in this paper, which recognises the behavior of pedestrians for each gait and improves the stride length estimation for PDR localisation based on the recognition results to reduce the accumulation of errors in the PDR localisation algorithm itself. At the same time, the system uses self‐researched hardware to modify the audio equipment used for broadcasting within the indoor environment, to locate the acoustic source through a Hamming distance‐based localisation algorithm, and to correct the estimated acoustic source estimated location based on the known source location in order to eliminate the accumulated error in PDR localisation. Through analysis and experimental verification, the recognition accuracy of pedestrian movement behavior recognition proposed in this paper reaches 95% and the acoustic source localisation accuracy of 0.32 m during movement, thus, producing an excellent effect on eliminating the cumulative error of PDR localisation.

랜드마크 및 층 감지 방법을 이용한 스마트폰 기반 실내 보행자 항법 시스템

In this paper, an indoor PDR(Pedestrian Dead Reckoning) system using landmark and floor detection is proposed. The proposed method uses the PDR technique with error correction based on detecting the landmark. The system consists of an improved PDR, the landmark recognition, and the floor detection components. The improved PDR component is a PDR system with an improved capability using the recognition of holding styles. The landmark recognition component detects whether the user walks on the landmarks. Two kinds of landmarks are defined including the WiFi and the turning landmarks. The methods for recognizing each landmark are developed. Whenever the landmark is detected, the system updates the position with the given position of the landmark, which means to remove the cumulative position error. In addition, an adaptive floor detection method using a barometer and a WiFi fingerprinting technique is suggested for tracking a pedestrian in a multi-floor building. The experimental results on a three-floor building with three subjects show the superior performance of the proposed system.

Pedestrian navigation system based on the inertial measurement unit sensor for outdoor and indoor environments

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

Magnetic-Map-Matching-Aided Pedestrian Navigation Using Outlier Mitigation Based on Multiple Sensors and Roughness Weighting.

This research proposes an algorithm that improves the position accuracy of indoor pedestrian dead reckoning, by compensating the position error with a magnetic field map-matching technique, using multiple magnetic sensors and an outlier mitigation technique based on roughness weighting factors. Since pedestrian dead reckoning using a zero velocity update (ZUPT) does not use position measurements but zero velocity measurements in a stance phase, the position error cannot be compensated, which results in the divergence of the position error. Therefore, more accurate pedestrian dead reckoning is achievable when the position measurements are used for position error compensation. Unfortunately, the position information cannot be easily obtained for indoor navigation, unlike in outdoor navigation cases. In this paper, we propose a method to determine the position based on the magnetic field map matching by using the importance sampling method and multiple magnetic sensors. The proposed method does not simply integrate multiple sensors but uses the normalization and roughness weighting method for outlier mitigation. To implement the indoor pedestrian navigation algorithm more accurately than in existing indoor pedestrian navigation, a 15th-order error model and an importance-sampling extended Kalman filter was utilized to correct the error of the map-matching-aided pedestrian dead reckoning (MAPDR). To verify the performance of the proposed indoor MAPDR algorithm, many experiments were conducted and compared with conventional pedestrian dead reckoning. The experimental results show that the proposed magnetic field MAPDR algorithm provides clear performance improvement in all indoor environments.

Smartphone-Based 3D Indoor Pedestrian Positioning through Multi-Modal Data Fusion.

Combining research areas of biomechanics and pedestrian dead reckoning (PDR) provides a very promising way for pedestrian positioning in environments where Global Positioning System (GPS) signals are degraded or unavailable. In recent years, the PDR systems based on a smartphone’s built-in inertial sensors have attracted much attention in such environments. However, smartphone-based PDR systems are facing various challenges, especially the heading drift, which leads to the phenomenon of estimated walking path passing through walls. In this paper, the 2D PDR system is implemented by using a pocket-worn smartphone, and then enhanced by introducing a map-matching algorithm that employs a particle filter to prevent the wall-crossing problem. In addition, to extend the PDR system for 3D applications, the smartphone’s built-in barometer is used to measure the pressure variation associated to the pedestrian’s vertical displacement. Experimental results show that the map-matching algorithm based on a particle filter can effectively solve the wall-crossing problem and improve the accuracy of indoor PDR. By fusing the barometer readings, the vertical displacement can be calculated to derive the floor transition information. Despite the inherent sensor noises and complex pedestrian movements, smartphone-based 3D pedestrian positioning systems have considerable potential for indoor location-based services (LBS).

Improved Smartphone-Based Indoor Pedestrian Dead Reckoning Assisted by Visible Light Positioning

Aiming at the problem of heavy preparation work and high cost of indoor positioning technology, this paper proposes a visible light positioning (VLP)-assisted smartphone-based pedestrian dead reckoning (PDR) indoor positioning technology. This technology combines the advantages of both PDR and VLP algorithms for indoor positioning. During the pedestrian position calculation, the method of using the averaging of mode to process the heading angle data is creatively used, which improves the positioning accuracy and increases reliable operation time. At the same time, the two-level verification method consisting of cyclic redundancy check and range constraint is implemented for VLP, which uses a LED carrying a modulation code as a beacon for absolute position and the camera of smartphone to capture/decode the LED image to obtain the absolute position and periodically correct the position of the PDR positioning, thereby avoiding the accumulation of PDR positioning technology errors. The scope and reliability of PDR are demonstrated. The experiments show that this method is a kind of low cost, high robust, and high precision indoor positioning technology.

A Real-Time Pedestrian Dead Reckoning System With FM-Aided Motion Mode Recognition

Accurate indoor pedestrian positioning has attracted extensive research attention along with the rising popularity of indoor location-based service. Among the indoor positioning methods, pedestrian dead reckoning (PDR) stands out for requiring neither expensive infrastructure nor laborious site survey. However, with time going on, the PDR method suffers from error accumulation problem, whereas the major cause of positioning error is usually heading deviation. To tackle the error-drifting problem, a real-time indoor PDR system using inertial sensors and the frequency-modulated radio receiver is designed in this paper. First, a random forest classifier combining frequency-modulated radio signal and inertial sensor signal is devised to achieve real-time identification of straight-line and turning mode of moving. Based on the classification result, the constraint is applied to headings in straight periods and true headings are extracted from accelerometer and magnetometer measurements to reduce heading deviation in straight periods. Field experiments have been conducted at three sites in two office buildings to evaluate the performance of the proposed system. The experimental results indicate error drifting can be effectively constrained by the proposed PDR system.

Pedestrian Dead Reckoning Using Pocket-Worn Smartphone

Indoor pedestrian dead reckoning (PDR) extends the location-based service (LBS) to environments, where GPS or beacon signals are degraded or unavailable. A practical PDR system should consider the absence of any infrastructure or prior knowledge of the environment. This paper presents a PDR system based on a pocket-worn smartphone, which tracks a person's location through dead reckoning calculation by using the sensors embedded in smartphones. However, a smartphone-based PDR system faces various challenges, especially the heading drift due to gyroscope bias. In this paper, the gradient descent algorithm (GDA) is improved to reduce the heading drift, by fusing inertial data with only a fraction of magnetometer data that are accurate and usable. There is an 80% probability that the heading error is reduced to less than 4 degrees. Besides, a stride detection method is developed based on thigh angles, and then, a stride length estimation method is implemented in a complementary way. The experiments were conducted along with three types of reference paths, and the experimental results demonstrate that the average position errors along the three paths are 1.62%, 1.00%, and 0.92%, respectively. Despite the inherent sensor noise and complex human locomotion, the smartphone-based PDR system has great potential in pedestrian tracking.

Optimal Heading Estimation Based Multidimensional Particle Filter for Pedestrian Indoor Positioning

Localization capability is a challenging task in global navigation satellite system-degraded or denied environments. Alternatively, today’s smartphones have an increased number of integrated sensors that can act as terminals for indoor personal positioning solutions such as pedestrian dead reckoning (PDR). However, magnetic interference, poor sensor measurements, and diverse handling of smartphone quickly decrease the performance for indoor PDR. This paper proposes a comprehensive and novel pedestrian indoor positioning solution in which heading estimation is improved by using simplified magnetometer calibration, by calculating projected acceleration along the moving direction using frequency-domain features and by applying direction constrains to indoor accessible paths. Moreover, compared with an ordinary particle filter (OPF) and a Kalman filter, this paper proposes a multidimensional particle filter (MPF) algorithm, namely MPF, which includes high-dimensional variables such as position, heading, step length parameters, motion label, lifetime, number of current particles, and factor. An MPF can handle more uncertain parameters than the OPF. Therefore, positioning with an MPF can achieve lower errors using low-quality sensors, mitigate interference introduced from surrounding environments, and reduce heading ambiguities due to different modes of carrying a smartphone. Consequently, field tests show that the proposed algorithm obtains robust performance for heading estimation and positioning.

Axis-Exchanged Compensation and Gait Parameters Analysis for High Accuracy Indoor Pedestrian Dead Reckoning

Pedestrian dead reckoning (PDR) is an effective way for navigation coupled with GNSS (Global Navigation Satellite System) or weak GNSS signal environment like indoor scenario. However, indoor location with an accuracy of 1 to 2 meters determined by PDR based on MEMS-IMU is still very challenging. For one thing, heading estimation is an important problem in PDR because of the singularities. For another thing, walking distance estimation is also a critical problem for pedestrian walking with randomness. Based on the above two problems, this paper proposed axis-exchanged compensation and gait parameters analysis algorithm to improve the navigation accuracy. In detail, an axis-exchanged compensation factored quaternion algorithm is put forward first to overcome the singularities in heading estimation without increasing the amount of computation. Besides, real-time heading is updated by R-adaptive Kalman filter. Moreover, gait parameters analysis algorithm can be divided into two steps: cadence detection and step length estimation. Thus, a method of cadence classification and interval symmetry is proposed to detect the cadence accurately. Furthermore, a step length model adjusted by cadence is established for step length estimation. Compared to the traditional PDR navigation, experimental results showed that the error of navigation reduces 32.6%.

Enhancing indoor inertial pedestrian navigation using a shoe-worn marker.

We propose a novel hybrid inertial sensors-based indoor pedestrian dead reckoning system, aided by computer vision-derived position measurements. In contrast to prior vision-based or vision-aided solutions, where environmental markers were used—either deployed in known positions or extracted directly from it—we use a shoe-fixed marker, which serves as positional reference to an opposite shoe-mounted camera during foot swing, making our system self-contained. Position measurements can be therefore more reliably fed to a complementary unscented Kalman filter, enhancing the accuracy of the estimated travelled path for 78%, compared to using solely zero velocities as pseudo-measurements.

Use of High Sensitivity GNSS Receiver Doppler Measurements for Indoor Pedestrian Dead Reckoning

Dead-reckoning (DR) algorithms, which use self-contained inertial sensors combined with gait analysis, have proven to be effective for pedestrian navigation purposes. In such DR systems, the primary error is often due to accumulated heading drifts. By tightly integrating global navigation satellite system (GNSS) Doppler measurements with DR, such accumulated heading errors can usually be accurately compensated. Under weak signal conditions, high sensitivity GNSS (HSGNSS) receivers with block processing techniques are often used, however, the Doppler quality of such receivers is relatively poor due to multipath, fading and signal attenuation. This often limits the benefits of integrating HSGNSS Doppler with DR. This paper investigates the benefits of using Doppler measurements from a novel direct vector HSGNSS receiver with pedestrian dead-reckoning (PDR) for indoor navigation. An indoor signal and multipath model is introduced which explains how conventional HSGNSS Doppler measurements are affected by indoor multipath. Velocity and Doppler estimated by using direct vector receivers are introduced and discussed. Real experimental data is processed and analyzed to assess the veracity of proposed method. It is shown when integrating HSGNSS Doppler with PDR algorithm, the proposed direct vector method are more helpful than conventional block processing method for the indoor environments considered herein.