Team Name

Next-Newbie Reckoners

Reference person

Seanglidet Yean

Affiliation

Singtel Cognitive and Artificial Intelligence Lab for Enterprises at NTU (SCALE@NTU); Nanyang Technological University (SG)

Description

We have adopted grid-based approach to train the Random Forest models with an end-to-end pipleline in order to predict the building, floor and location grid using the WiFi Received Signal Strength. In addition, we are working on the Deep Neural Network models as well as feature selection methods to further improve the accuracy.
In this competition, our main objective is to incorporate multi-source of information such as Bluetooth, Smartphone sensor-fusion techniques (e.g. pedestrian dead reckoning) to enhance our method.

Team Name

WHU-five

Reference person

Li Wei

Affiliation

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), WuHan University (CN)

Description

Training: According to the training data set, establish a geomagnetic fingerprint library, a WIFI fingerprint library, and a lighting library.
Test: Estimate the initial position through WIFI fingerprint positioning, lights, geomagnetism, etc., and then merge the results of PDR, geomagnetism, and wifi positioning to get the final position.
The floor can be judged roughly based on the barometer. When there is a weak change in air pressure between floors, the floor information can be detected by fusing other signals (wifi, geomagnetism, lights, etc.).

Team Name

YAI

Reference person

ChihChieh Yu

Affiliation

Department of Electrical Engineering, Yuan Ze University and National Ilan University (TW)

Description

Our fingerprinting is based on Wi-Fi signal strength (RSS) values expressed in dBm. In this competition, we propose a fuzzy-based pre-processing method so that the RSS entries in the fingerprinting database can be converted into the corresponding defuzzification values. The fuzzy-based pre-processing utilizes five membership functions on the received RSS signals and the parameters associated with these membership functions are trained by the training dataset. Finally, we obtain a non-linear mapping function, which mapping the RSS values to the real values between the range of 0 to 4 as shown in Fig. 1. More specifically, we adopt air pressure value for floor judgment, acceleration value for step detection, and Wi-Fi signal for fingerprinting table positioning.

Team Name

Indora

Reference person

Miroslav Opiela

Affiliation

Institute of Computer Science, P.J. Šafárik University, Faculty of Science, Jesenná 5, 041 54 Košice (SK)

Pedestrian dead-reckoning, map model and bayesian filtering are essential components of the proposed localization system. The main research focus is on a low-dimensional grid-based bayesian filtering, as a less elaborated alternative to Kalman and Particle filters widely used for the positioning. A semi-automatically generated map model helps to reduce the localization error introduced by noisy sensor measurements and inaccurate system configuration, e.g., a step length estimation. The floor transition detection based on barometer measurements allows the system to estimate the user position within a single floor. Step length estimation is incorporated in the particle filter. The light sensor improves the ability to distinguish between two similar corridors and detect indoor-outdoor transitions. The solution is based on a Wi-Fi radio map built with the signals available in the training dataset. It uses sensor fusion to merge the results obtained from the Wi-Fi fingerprinting with data from other sensors, including accelerometers records to detect the person steps, the mobile phone 3D orientation records for heading estimation and pressure sensors for floor transition detection. Our system consists of 4 parts. The first part uses intertial sensordata to detect steps, estimate step length and heading estimation and to recognize walking modes. The second part uses the PDR algorithm to interpolate between the sparse ground truth positions. The interpolated positions are used to create a radiomap. The third part uses the barometer and RSS measurements from the training data to detect floor transitions, to estimate during which steps the user is using the stairs and to calculate the most likely sequence of floors visited by the user. In the final part, a backtracking particle filter (BPF) is used to estimate the trajectory. The step lengths and headings are used to propagate the particles. The positions of walls are used to remove particles that were propagated through a wall. The weighted centroid of the k best RSS matches is used to weigh each particle, based on its distance to the matched position. The locations of staircases are used to decrease the weight of particles outside of staircases when the barometer detects a height change. The floor transitions are used to load the next floor map and radiomap. A smartphone integrates rich sensors, including inertial sensor, magnetic sensor, light sensor, sound sensor and proximity sensor, etc. Therefore, it provides a possibility to localize pedestrians, which is valuable and challenging. In this competition, step-length and heading system (SHS) is used as our fundamental system. Acceleration information from inertial sensor is used to detect steps. Then the step length can be computed. To our best knowledge, step-length computing is accurate when a pedestrian moves forward on a level ground. However, the estimated step length becomes inaccurate when a pedestrian moves on a slope, such as walking upstairs or downstairs. To address the problem, we propose adaptive step-length estimation method aided by learning-based motion classifier. Since the performance of the SHS degrades with time and walking distance, reference positions should be introduced to modify the positioning error of the system. Therefore, we establish magnetic field and Wi-Fi-based fingerprint databases with the training and validation data. Then a neural network (NN) model is trained to predict the pre-given ground truth positions using geomagnetic information and Wi-Fi received signal strength. Finally, Kalman filter is leveraged to fuse the positioning results from the NN model and the SHS. We have used a stack of complementary localization components that were intelligently fused together to provide a final actor’s itinerary.
The main components are: floor detection, user activity recognition, speed/step length estimation, PDR, WiFi based localization and, finally, a novel component of magnetic field based localization.
Floor detection was done using classic classification methods based on WiFi and barometer data. We have applied spectral analysis on the accelerometer data in order to identify user's activity (walking, standing, going up or down the stairs). Next, we have used peaks detection of accelerometer data in order to detect steps. We extract acceleration features in order to learn the step length / speed in a given sliding window. Together with the orientation sensor it allowed us to build a PDR which was a first order approximation of the itinerary.
The PDR is known for being accurate locally but drifting over time. In order to compensate for this drift, we have used global localization techniques based on WiFi VAE that was presented at IPIN’19 and recently developed LSTM based magnetic field localization algorithm.
Finally, the itinerary was adjusted using a map that was extracted from the training data. The XMU_PDR system is a multi-sensor fusion system for indoor positioning. It fuses the information provided by inertial measurement unit, magnetometer, barometer, WIFI, GNSS and light sensor and the maps. It calculates the PDR trajectory using the data of accelerometer and gyroscope firstly. By using all the sensors information, the ubiquitous context awareness positions were found out to eliminate the accumulated drift error. The magnetic fingerprint was collected using the sensor data in the training set. The dynamic time warping algorithm is used to match the trajectory with the trajectories in the training set. At the same time, the trajectory is corrected according to the map information, and the optimal estimation under the map constraint is realized according to the constraint of walls and special positions such as the staircase and doorway. The barometer data and WiFi data in the training set are also used to predict the changes of floor. 1. WiFi Fingerprint positioning: for non-machine-learning algorithms, in the offline phase a RSSI ranking-based hierarchical radio map is constructed, with help of AP selection algorithms; in the online phase, RSSI ranking-based location fingerprinting method that uses Kendall Tau Correlation Coefficient (KTCC) and Convolutional Neural Networks (CNN) are used to determine the user’s location, and magnetic field-based path matching is used.
For machine/deep learning algorithms, we build the training set by using all APs’ RSSI information at known points; training our models (e.g SVM, XGBoost, KNN, CNN); predicting the positions of unknown points.
2. Pedestrian Dead Reckoning (PDR): step counter; pose estimator updated by using the quaternion method, besides, AHRS contains the information of the quaternion, which can be used to fuse with estimated heading.
3. Mobility Detection: we build the training set by collecting the data from provided training and validate sets; Train our models (e.g SVM, XGBoost, DNN, CNN); predict the mobility modes of the user’s state.
4. Floor Level Detection: similar to mobility detection.
5. Indoor/Outdoor Detection: we use GNSS to discriminate indoor scenarios and outdoor scenarios using machine learning algorithms.
6. Data Fusion: extended Kalman Filter or Unascend Kalman Filter or Particle Filter. We use a neural network-based indoor positioning based on pedestrian dead reckoning and particle filter to account for the influence on positioning of the indoor spatial map. In the training process, the smartphone built-in sensors are used as data sources to provide fine-grained position observations. The sparse Wi-Fi signal are trained for regressing a position which label is provide by PDR mothed. In the test process, Wi-Fi signal are used as observations in the particle filter.

Track 4 - Foot-mounted IMU (off-site)

Team Name

WHUGNSS

Team members

Jian Kuang, Tao Liu, Yan Wang

Affiliation

Wuhan University (CN)

Description

The classic zero-velocity update algorithm (ZUPT) based foot-mounted pedestrian dead reckoning consists of a strap-down inertial navigation algorithm, a stance phase detection algorithm, and an error state Kalman filter. However, the classic ZUPT-based Foot-PDR cannot be overcome the influence of the complex motion of the pedestrian. Several schemes are designed to improve navigation performance. 1) An improved adaptive threshold algorithm to detect the stance-phase in each gait cycle. 2) The zero angular rate update (ZARU) algorithm, the improved heuristic drift elimination (iHDE), and the straight-line constraint algorithm are used to constraint the heading error drift. 3) A motion detection algorithm is used to distinguish ground, escalator, and elevator, and a constant speed constraint is used to update the velocity vector when the pedestrian takes the escalator and elevator. 4) The calibrated magnetometer observations are used to detect whether the user returns to the same area. 5) The loosely integrated model is used to combine Foot-PDR and GNSS signals, and an adaptively robust algorithm is used to improve the performance of the Kalman filter. 6) The optimal inertial sensor parameters (i.e., the bias instability of gyroscopes and accelerometers, the angular random walk, and the velocity random walk) are determined through the provided long-term static data.
Team Name

BHSNIP

Team members

Ming Xia, Dayu Yan, Yuhang Li, Yitong Dong and Haitao Jiang

Affiliation

Beihang University (CN)

Description

Researchers usually suppress the divergence of positioning error within IEZ (Inertial Navigation System - Extended Kalman Filter - Zero Velocity Update, INS-EKF-ZUPT) framework. However, due to the poor observability of heading errors to ZUPT and the instability of vertical inertial channels, further corrections of the estimated trajectories under the IEZ framework are still needed to obtain higher positioning accuracy. First, the improved Step Height Equidistant (SHE) method was exploited to estimate the altitude, which was based on vertical motion modes. Then, the Strapdown Inertial Navigation System (SINS) and GPS was integrated according to loose combination. Furthermore, the improved heuristic drift elimination was also used to constraint the heading drift. Finally, the adaptive method was utilized to adjust the parameters of EKF and the threshold of ZUPT.
Team Name

Free-Walking

Team members

Haiyong Luo, Qu Wang

Affiliation

Beijing University of Posts and Telecommunications; Institute of Computing Technology, Chinese Academy of Sciences (CN)

Description

A foot-mounted pedestrian inertial navigation system that accurately tracks pedestrian position by using inertial measurement units (IMUs) embedded in device when pedestrian walking normally. However, the positioning accuracy is decreased under complex movements. In this work, we divide the commonly used indoor walking motion type into eight modes construct motion mode classifier based on stacked denoising autoencoder and temporal convolutional network with attention to recognize these pedestrian motion modes. Base on the walking mode classification, we optimize the threshold or parameters of strapdown inertial navigation and zero-velocity detection, and Kalman filter for each walking mode. The proposed method can more effectively distinguish the pedestrian’s walking mode, accurately detect the stationary phase and estimate the device attitude under various pedestrian movements. The results of multi-floor and mixed walking modes experiment show that the positioning errors of the proposed method are less than 2%.
Team Name

AIR

Reference person

Wenchao Zhang

Affiliation

Aerospace Information Research Institute, Chinese Academy of Sciences (CN)

Description

Still Phase Detection includes two components: the GLRT detector algorithm used under the condition of the slow and normal pedestrian gait speed, and the HMM detector algorithm used under the condition of the dynamic and fast pedestrian gait speed. After that, using the improved HDE and HUPT method to estimate current position errors, ZUPT is used to estimate the velocity error, while Earth Magnetic Yaw based on QSF method is used to estimate the heading error.

A gait or a walk cycle consists of two phases: the swing and stance phase. In the swing phase, the foot is not in contact with the ground. In contrast, the foot contacts the ground in the stance phase  GLRT algorithm has obvious advantages for zero speed detection of stable pedestrian gait velocity, while HMM algorithm has a good effect for zero speed detection of dynamic and fast pedestrian gait speed  Thus, the two methods are combined to achieve the dynamic human stance.

Track 5 - xDR in manufacturing (off-site)

Team Name

Kawaguchi Lab

Reference person

Takuto Yoshida

Affiliation

Nagoya university (JP)

Description

A robust xDR for a target to track based on a velocity estimation method using a neural network. A trajectory estimation by this method consists of the following three steps: 1) an end-to-end speed estimation using a neural network based on LSTM. 2) A heading estimation by integrating angular velocity on the z-axis which is a projection of angular velocity to gravity. 3) Correcting the trajectory calculated from speed and heading using BLE beacon signal, reference point, and map. Our challenge is how to extend the velocity estimation method originally intended for PDR to VDR.
Team Name

yonayona

Reference person

Yoshitomo Yonamoto

Affiliation

Keio university (JP)

Description

My main method is to emphasis on absolute positioning technology. In a certain of time, I use more than 3 detected beacons, try various approaches such as average weighted and trilateration. and choose best estimation.
I also tackle to decrease negative check points by implementing prevent-intersect-wall algorithm.

Team Name

WHU-Autonavi

Reference person

Jian Kuang

Affiliation

Wuhan university;  AutoNavi Software Co., Ltd. (CN)

Team Name

Next-Newbie Reckoners

Reference person

Seanglidet Yean

Affiliation

Singtel Cognitive and Artificial Intelligence Lab for Enterprises at NTU (SCALE@NTU); Nanyang Technological University (SG)

Description

We have adopted grid-based approach to train the Random Forest models with an end-to-end pipleline in order to predict the building, floor and location grid using the WiFi Received Signal Strength. In addition, we are working on the Deep Neural Network models as well as feature selection methods to further improve the accuracy.
In this competition, our main objective is to incorporate multi-source of information such as Bluetooth, Smartphone sensor-fusion techniques (e.g. pedestrian dead reckoning) to enhance our method.

Team Name

WHU-five

Reference person

Li Wei

Affiliation

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), WuHan University (CN)

Description

Training: According to the training data set, establish a geomagnetic fingerprint library, a WIFI fingerprint library, and a lighting library.
Test: Estimate the initial position through WIFI fingerprint positioning, lights, geomagnetism, etc., and then merge the results of PDR, geomagnetism, and wifi positioning to get the final position.
The floor can be judged roughly based on the barometer. When there is a weak change in air pressure between floors, the floor information can be detected by fusing other signals (wifi, geomagnetism, lights, etc.).

Team Name

YAI

Reference person

ChihChieh Yu

Affiliation

Department of Electrical Engineering, Yuan Ze University and National Ilan University (TW)

Description

Our fingerprinting is based on Wi-Fi signal strength (RSS) values expressed in dBm. In this competition, we propose a fuzzy-based pre-processing method so that the RSS entries in the fingerprinting database can be converted into the corresponding defuzzification values. The fuzzy-based pre-processing utilizes five membership functions on the received RSS signals and the parameters associated with these membership functions are trained by the training dataset. Finally, we obtain a non-linear mapping function, which mapping the RSS values to the real values between the range of 0 to 4 as shown in Fig. 1. More specifically, we adopt air pressure value for floor judgment, acceleration value for step detection, and Wi-Fi signal for fingerprinting table positioning.

Team Name

Indora

Reference person

Miroslav Opiela

Affiliation

Institute of Computer Science, P.J. Šafárik University, Faculty of Science, Jesenná 5, 041 54 Košice (SK)

Pedestrian dead-reckoning, map model and bayesian filtering are essential components of the proposed localization system. The main research focus is on a low-dimensional grid-based bayesian filtering, as a less elaborated alternative to Kalman and Particle filters widely used for the positioning. A semi-automatically generated map model helps to reduce the localization error introduced by noisy sensor measurements and inaccurate system configuration, e.g., a step length estimation. The floor transition detection based on barometer measurements allows the system to estimate the user position within a single floor. Step length estimation is incorporated in the particle filter. The light sensor improves the ability to distinguish between two similar corridors and detect indoor-outdoor transitions. The solution is based on a Wi-Fi radio map built with the signals available in the training dataset. It uses sensor fusion to merge the results obtained from the Wi-Fi fingerprinting with data from other sensors, including accelerometers records to detect the person steps, the mobile phone 3D orientation records for heading estimation and pressure sensors for floor transition detection. Our system consists of 4 parts. The first part uses intertial sensordata to detect steps, estimate step length and heading estimation and to recognize walking modes. The second part uses the PDR algorithm to interpolate between the sparse ground truth positions. The interpolated positions are used to create a radiomap. The third part uses the barometer and RSS measurements from the training data to detect floor transitions, to estimate during which steps the user is using the stairs and to calculate the most likely sequence of floors visited by the user. In the final part, a backtracking particle filter (BPF) is used to estimate the trajectory. The step lengths and headings are used to propagate the particles. The positions of walls are used to remove particles that were propagated through a wall. The weighted centroid of the k best RSS matches is used to weigh each particle, based on its distance to the matched position. The locations of staircases are used to decrease the weight of particles outside of staircases when the barometer detects a height change. The floor transitions are used to load the next floor map and radiomap. A smartphone integrates rich sensors, including inertial sensor, magnetic sensor, light sensor, sound sensor and proximity sensor, etc. Therefore, it provides a possibility to localize pedestrians, which is valuable and challenging. In this competition, step-length and heading system (SHS) is used as our fundamental system. Acceleration information from inertial sensor is used to detect steps. Then the step length can be computed. To our best knowledge, step-length computing is accurate when a pedestrian moves forward on a level ground. However, the estimated step length becomes inaccurate when a pedestrian moves on a slope, such as walking upstairs or downstairs. To address the problem, we propose adaptive step-length estimation method aided by learning-based motion classifier. Since the performance of the SHS degrades with time and walking distance, reference positions should be introduced to modify the positioning error of the system. Therefore, we establish magnetic field and Wi-Fi-based fingerprint databases with the training and validation data. Then a neural network (NN) model is trained to predict the pre-given ground truth positions using geomagnetic information and Wi-Fi received signal strength. Finally, Kalman filter is leveraged to fuse the positioning results from the NN model and the SHS. We have used a stack of complementary localization components that were intelligently fused together to provide a final actor’s itinerary.
The main components are: floor detection, user activity recognition, speed/step length estimation, PDR, WiFi based localization and, finally, a novel component of magnetic field based localization.
Floor detection was done using classic classification methods based on WiFi and barometer data. We have applied spectral analysis on the accelerometer data in order to identify user's activity (walking, standing, going up or down the stairs). Next, we have used peaks detection of accelerometer data in order to detect steps. We extract acceleration features in order to learn the step length / speed in a given sliding window. Together with the orientation sensor it allowed us to build a PDR which was a first order approximation of the itinerary.
The PDR is known for being accurate locally but drifting over time. In order to compensate for this drift, we have used global localization techniques based on WiFi VAE that was presented at IPIN’19 and recently developed LSTM based magnetic field localization algorithm.
Finally, the itinerary was adjusted using a map that was extracted from the training data. The XMU_PDR system is a multi-sensor fusion system for indoor positioning. It fuses the information provided by inertial measurement unit, magnetometer, barometer, WIFI, GNSS and light sensor and the maps. It calculates the PDR trajectory using the data of accelerometer and gyroscope firstly. By using all the sensors information, the ubiquitous context awareness positions were found out to eliminate the accumulated drift error. The magnetic fingerprint was collected using the sensor data in the training set. The dynamic time warping algorithm is used to match the trajectory with the trajectories in the training set. At the same time, the trajectory is corrected according to the map information, and the optimal estimation under the map constraint is realized according to the constraint of walls and special positions such as the staircase and doorway. The barometer data and WiFi data in the training set are also used to predict the changes of floor. 1. WiFi Fingerprint positioning: for non-machine-learning algorithms, in the offline phase a RSSI ranking-based hierarchical radio map is constructed, with help of AP selection algorithms; in the online phase, RSSI ranking-based location fingerprinting method that uses Kendall Tau Correlation Coefficient (KTCC) and Convolutional Neural Networks (CNN) are used to determine the user’s location, and magnetic field-based path matching is used.
For machine/deep learning algorithms, we build the training set by using all APs’ RSSI information at known points; training our models (e.g SVM, XGBoost, KNN, CNN); predicting the positions of unknown points.
2. Pedestrian Dead Reckoning (PDR): step counter; pose estimator updated by using the quaternion method, besides, AHRS contains the information of the quaternion, which can be used to fuse with estimated heading.
3. Mobility Detection: we build the training set by collecting the data from provided training and validate sets; Train our models (e.g SVM, XGBoost, DNN, CNN); predict the mobility modes of the user’s state.
4. Floor Level Detection: similar to mobility detection.
5. Indoor/Outdoor Detection: we use GNSS to discriminate indoor scenarios and outdoor scenarios using machine learning algorithms.
6. Data Fusion: extended Kalman Filter or Unascend Kalman Filter or Particle Filter. We use a neural network-based indoor positioning based on pedestrian dead reckoning and particle filter to account for the influence on positioning of the indoor spatial map. In the training process, the smartphone built-in sensors are used as data sources to provide fine-grained position observations. The sparse Wi-Fi signal are trained for regressing a position which label is provide by PDR mothed. In the test process, Wi-Fi signal are used as observations in the particle filter.

Track 4 - Foot-mounted IMU (off-site)

Team Name

WHUGNSS

Team members

Jian Kuang, Tao Liu, Yan Wang

Affiliation

Wuhan University (CN)

Description

The classic zero-velocity update algorithm (ZUPT) based foot-mounted pedestrian dead reckoning consists of a strap-down inertial navigation algorithm, a stance phase detection algorithm, and an error state Kalman filter. However, the classic ZUPT-based Foot-PDR cannot be overcome the influence of the complex motion of the pedestrian. Several schemes are designed to improve navigation performance. 1) An improved adaptive threshold algorithm to detect the stance-phase in each gait cycle. 2) The zero angular rate update (ZARU) algorithm, the improved heuristic drift elimination (iHDE), and the straight-line constraint algorithm are used to constraint the heading error drift. 3) A motion detection algorithm is used to distinguish ground, escalator, and elevator, and a constant speed constraint is used to update the velocity vector when the pedestrian takes the escalator and elevator. 4) The calibrated magnetometer observations are used to detect whether the user returns to the same area. 5) The loosely integrated model is used to combine Foot-PDR and GNSS signals, and an adaptively robust algorithm is used to improve the performance of the Kalman filter. 6) The optimal inertial sensor parameters (i.e., the bias instability of gyroscopes and accelerometers, the angular random walk, and the velocity random walk) are determined through the provided long-term static data.
Team Name

BHSNIP

Team members

Ming Xia, Dayu Yan, Yuhang Li, Yitong Dong and Haitao Jiang

Affiliation

Beihang University (CN)

Description

Researchers usually suppress the divergence of positioning error within IEZ (Inertial Navigation System - Extended Kalman Filter - Zero Velocity Update, INS-EKF-ZUPT) framework. However, due to the poor observability of heading errors to ZUPT and the instability of vertical inertial channels, further corrections of the estimated trajectories under the IEZ framework are still needed to obtain higher positioning accuracy. First, the improved Step Height Equidistant (SHE) method was exploited to estimate the altitude, which was based on vertical motion modes. Then, the Strapdown Inertial Navigation System (SINS) and GPS was integrated according to loose combination. Furthermore, the improved heuristic drift elimination was also used to constraint the heading drift. Finally, the adaptive method was utilized to adjust the parameters of EKF and the threshold of ZUPT.
Team Name

Free-Walking

Team members

Haiyong Luo, Qu Wang

Affiliation

Beijing University of Posts and Telecommunications; Institute of Computing Technology, Chinese Academy of Sciences (CN)

Description

A foot-mounted pedestrian inertial navigation system that accurately tracks pedestrian position by using inertial measurement units (IMUs) embedded in device when pedestrian walking normally. However, the positioning accuracy is decreased under complex movements. In this work, we divide the commonly used indoor walking motion type into eight modes construct motion mode classifier based on stacked denoising autoencoder and temporal convolutional network with attention to recognize these pedestrian motion modes. Base on the walking mode classification, we optimize the threshold or parameters of strapdown inertial navigation and zero-velocity detection, and Kalman filter for each walking mode. The proposed method can more effectively distinguish the pedestrian’s walking mode, accurately detect the stationary phase and estimate the device attitude under various pedestrian movements. The results of multi-floor and mixed walking modes experiment show that the positioning errors of the proposed method are less than 2%.
Team Name

AIR

Reference person

Wenchao Zhang

Affiliation

Aerospace Information Research Institute, Chinese Academy of Sciences (CN)

Description

Still Phase Detection includes two components: the GLRT detector algorithm used under the condition of the slow and normal pedestrian gait speed, and the HMM detector algorithm used under the condition of the dynamic and fast pedestrian gait speed. After that, using the improved HDE and HUPT method to estimate current position errors, ZUPT is used to estimate the velocity error, while Earth Magnetic Yaw based on QSF method is used to estimate the heading error.

A gait or a walk cycle consists of two phases: the swing and stance phase. In the swing phase, the foot is not in contact with the ground. In contrast, the foot contacts the ground in the stance phase  GLRT algorithm has obvious advantages for zero speed detection of stable pedestrian gait velocity, while HMM algorithm has a good effect for zero speed detection of dynamic and fast pedestrian gait speed  Thus, the two methods are combined to achieve the dynamic human stance.

Track 5 - xDR in manufacturing (off-site)

Team Name

Kawaguchi Lab

Reference person

Takuto Yoshida

Affiliation

Nagoya university (JP)

Description

A robust xDR for a target to track based on a velocity estimation method using a neural network. A trajectory estimation by this method consists of the following three steps: 1) an end-to-end speed estimation using a neural network based on LSTM. 2) A heading estimation by integrating angular velocity on the z-axis which is a projection of angular velocity to gravity. 3) Correcting the trajectory calculated from speed and heading using BLE beacon signal, reference point, and map. Our challenge is how to extend the velocity estimation method originally intended for PDR to VDR.
Team Name

yonayona

Reference person

Yoshitomo Yonamoto

Affiliation

Keio university (JP)

Description

My main method is to emphasis on absolute positioning technology. In a certain of time, I use more than 3 detected beacons, try various approaches such as average weighted and trilateration. and choose best estimation.
I also tackle to decrease negative check points by implementing prevent-intersect-wall algorithm.

Team Name

WHU-Autonavi

Reference person

Jian Kuang

Affiliation

Wuhan university;  AutoNavi Software Co., Ltd. (CN)