Hybrid indoor localization scheme with image sensor-based visible light positioning and pedestrian dead reckoning

2019 ◽  
Vol 58 (12) ◽  
pp. 3214 ◽  
Author(s):  
Heqing Huang ◽  
Bo Lin ◽  
Lihui Feng ◽  
Huichao Lv
Keyword(s):  
Visible Light ◽  
Indoor Localization ◽  
Dead Reckoning ◽  
Image Sensor ◽  
Localization Scheme ◽  
Pedestrian Dead Reckoning

scholarly journals High-Accuracy Robot Indoor Localization Scheme Based on Robot Operating System Using Visible Light Positioning

2020 ◽  
Vol 12 (2) ◽  
pp. 1-16 ◽  
Author(s):  
Weipeng Guan ◽  
Shihuan Chen ◽  
Shangsheng Wen ◽  
Zequn Tan ◽  
Hongzhan Song ◽  
...  
Keyword(s):  
Operating System ◽  
Visible Light ◽  
Indoor Localization ◽  
High Accuracy ◽  
Robot Operating System ◽  
Localization Scheme

scholarly journals A Loose-Coupled Fusion of Inertial and UWB Assisted by a Decision-Making Algorithm for Localization of Emergency Responders

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1463 ◽  
Author(s):  
André G. Ferreira ◽  
Duarte Fernandes ◽  
André P. Catarino ◽  
Ana M. Rocha ◽  
João L. Monteiro
Keyword(s):  
Decision Making ◽  
Indoor Localization ◽  
Dead Reckoning ◽  
Time Of Arrival ◽  
Robust Solution ◽  
Localization Accuracy ◽  
Positioning Systems ◽  
Emergency Responders ◽  
Pedestrian Dead Reckoning ◽  
Non Line Of Sight

Combining different technologies is gaining significant popularity among researchers and industry for the development of indoor positioning systems (IPSs). These hybrid IPSs emerge as a robust solution for indoor localization as the drawbacks of each technology can be mitigated or even eliminated by using complementary technologies. However, fusing position estimates from different technologies is still very challenging and, therefore, a hot research topic. In this work, we pose fusing the ultrawideband (UWB) position estimates with the estimates provided by a pedestrian dead reckoning (PDR) by using a Kalman filter. To improve the IPS accuracy, a decision-making algorithm was developed that aims to assess the usability of UWB measurements based on the identification of non-line-of-sight (NLOS) conditions. Three different data fusion algorithms are tested, based on three different time-of-arrival positioning algorithms, and experimental results show a localization accuracy of below 1.5 m for a 99th percentile.

scholarly journals iPAC: Integrate Pedestrian Dead Reckoning and Computer Vision for Indoor Localization and Tracking

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 183514-183523 ◽  
Author(s):  
Danyang Li ◽  
Yumeng Lu ◽  
Jingao Xu ◽  
Qiang Ma ◽  
Zhuo Liu
Keyword(s):  
Computer Vision ◽  
Indoor Localization ◽  
Dead Reckoning ◽  
Localization And Tracking ◽  
Pedestrian Dead Reckoning

Accurate Dead Reckoning Localization System Using RFID Phase

2014 ◽  
Vol 513-517 ◽  
pp. 3296-3299 ◽  
Author(s):  
Bo Li ◽  
Dong Wang
Keyword(s):  
Radio Frequency Identification ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Dead Reckoning ◽  
Location Based Service ◽  
Rfid Tag ◽  
Whole Process ◽  
Localization Scheme ◽  
Frequency Identification ◽  
Series Of Experiments

Nowadays, the demands of Location-based Service are growing fast. It contains huge business opportunities. This paper presents an efficient indoor localization scheme using Radio-Frequency Identification technology. The major idea of our method is Dead Reckoning, a method of navigation that using the best estimates of speed and direction to calculate users' motion trace. We implemented Dead Reckoning in indoor environment by taking advantage of features of RFID. We collected RFID tag phase value to calculate the velocity of users and recalibrate users' position by using known fixed RFID reader. We designed a series of experiments to verify the feasibility of our velocity calculation method, then we simulated the whole process of our system. The results show that our system can track user's motion effectively in indoor environment. We believe this is an encouraging result, holding promise for real-world deployment.

PDR-Aided Algorithm with WiFi Fingerprint Matching for Indoor Localization

2014 ◽  
Vol 701-702 ◽  
pp. 989-993
Author(s):  
Wen Bin Yu ◽  
Peng Li ◽  
Zhi Chen ◽  
Chang Li
Keyword(s):  
Social Network ◽  
Indoor Localization ◽  
Dead Reckoning ◽  
Location Based Services ◽  
Fingerprint Matching ◽  
Network Applications ◽  
Wireless Signal ◽  
Signal Fluctuation ◽  
Pedestrian Dead Reckoning ◽  
Wifi Fingerprint

Recently, indoor localization is essential to enable location-based services for many mobile and social network applications. Due to fluctuation of the wireless signal, the accuracy of a simple WiFi fingerprint-based localization is not high. In this paper, we first exploit Pedestrian Dead Reckoning (PDR) technology to overcome the problem of the wireless signal fluctuation, then propose a PDR-aided algorithm with WiFi fingerprint matching for indoor localization, which using the PDR technology aided indoor localization. Experiments show that our algorithm has better accuracy than other indoor localization methods.

scholarly journals INDOOR LOCALIZATION USING ACCELEROMETER AND GYROSCOPE SMARTPHONE BASED

2021 ◽  
Vol 2 (2) ◽  
pp. 119-126
Author(s):  
Anggreini Intan Permata Sari ◽  
Arkham Zahri Rakhman
Keyword(s):  
Error Rate ◽  
Indoor Localization ◽  
Dead Reckoning ◽  
Initial Position ◽  
Sensor System ◽  
System Testing ◽  
Accelerometer Sensor ◽  
Campus Environment ◽  
Gyroscope Sensor ◽  
Pedestrian Dead Reckoning

Indoor localization is one of the more accurate technologies to be used to determine indoors or buildings. Pedestrian Dead Reckoning (PDR) is a method of determining the user's position by adding a method that occurs to a known initial position. The displacement that occurs is estimated with the help of an accelerometer sensor attached to the user as a step detector and to determine the direction towards the user using a gyroscope sensor. System testing is carried out in the Institut Teknologi Sumatera’s campus environment on the 2nd floor of Building C and D. The results from the detection of steps get an error rate of 1.13% using a threshold of 0.8.

scholarly journals Smartphone-Based Pedestrian Dead Reckoning for 3D Indoor Positioning

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8180
Author(s):  
Jijun Geng ◽  
Linyuan Xia ◽  
Jingchao Xia ◽  
Qianxia Li ◽  
Hongyu Zhu ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Indoor Localization ◽  
Dead Reckoning ◽  
Step Length ◽  
Indoor Positioning ◽  
Actual Measurement ◽  
Location Service ◽  
Cubature Kalman Filter ◽  
Robust Adaptive ◽  
Pedestrian Dead Reckoning

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.

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