scholarly journals Real-Time Pedestrian Tracking Terminal Based on Adaptive Zero Velocity Update

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3808
Author(s):  
Ran Wei ◽  
Hongda Xu ◽  
Mingkun Yang ◽  
Xinguo Yu ◽  
Zhuoling Xiao ◽  
...  
Keyword(s):  
Real Time ◽  
Real World ◽  
Inertial Measurement Unit ◽  
Dead Reckoning ◽  
Measurement Unit ◽  
Tracking Accuracy ◽  
Zero Velocity ◽  
And Performance ◽  
Pedestrian Dead Reckoning ◽  
Motion Types

In the field of pedestrian dead reckoning (PDR), the zero velocity update (ZUPT) method with an inertial measurement unit (IMU) is a mature technology to calibrate dead reckoning. However, due to the complex walking modes of different individuals, it is essential and challenging to determine the ZUPT conditions, which has a direct and significant influence on the tracking accuracy. In this research, we adopted an adaptive zero velocity update (AZUPT) method based on convolution neural networks to classify the ZUPT conditions. The AZUPT model was robust regardless of the different motion types of various individuals. AZUPT was then implemented on the Zynq-7000 SoC platform to work in real time to validate its computational efficiency and performance superiority. Extensive real-world experiments were conducted by 60 different individuals in three different scenarios. It was demonstrated that the proposed system could work equally well in different environments, making it portable for PDR to be widely performed in various real-world situations.

scholarly journals Multi-Floor Indoor Pedestrian Dead Reckoning with a Backtracking Particle Filter and Viterbi-Based Floor Number Detection

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4565
Author(s):  
Cedric De Cock ◽  
Wout Joseph ◽  
Luc Martens ◽  
Jens Trogh ◽  
David Plets
Keyword(s):  
Particle Filter ◽  
Real Time ◽  
Clustering Algorithm ◽  
Dead Reckoning ◽  
Detection Algorithm ◽  
Measurement Unit ◽  
Batch Mode ◽  
Access Points ◽  
Median Position ◽  
Pedestrian Dead Reckoning

We present a smartphone-based indoor localisation system, able to track pedestrians over multiple floors. The system uses Pedestrian Dead Reckoning (PDR), which exploits data from the smartphone’s inertial measurement unit to estimate the trajectory. The PDR output is matched to a scaled floor plan and fused with model-based WiFi received signal strength fingerprinting by a Backtracking Particle Filter (BPF). We proposed a new Viterbi-based floor detection algorithm, which fuses data from the smartphone’s accelerometer, barometer and WiFi RSS measurements to detect stairs and elevator usage and to estimate the correct floor number. We also proposed a clustering algorithm on top of the BPF to solve multimodality, a known problem with particle filters. The proposed system relies on only a few pre-existing access points, whereas most systems assume or require the presence of a dedicated localisation infrastructure. In most public buildings and offices, access points are often available at smaller densities than used for localisation. Our system was extensively tested in a real office environment with seven 41 m × 27 m floors, each of which had two WiFi access points. Our system was evaluated in real-time and batch mode, since the system was able to correct past states. The clustering algorithm reduced the median position error by 17% in real-time and 13% in batch mode, while the floor detection algorithm achieved a 99.1% and 99.7% floor number accuracy in real-time and batch mode, respectively.

scholarly journals RTK/Pseudolite/LAHDE/IMU-PDR Integrated Pedestrian Navigation System for Urban and Indoor Environments

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1791
Author(s):  
Ruihui Zhu ◽  
Yunjia Wang ◽  
Hongji Cao ◽  
Baoguo Yu ◽  
Xingli Gan ◽  
...  
Keyword(s):  
Real Time ◽  
High Precision ◽  
Navigation System ◽  
Carrier Phase ◽  
Inertial Measurement Unit ◽  
Dead Reckoning ◽  
Measurement Unit ◽  
Indoor Environments ◽  
Inertial Measurement ◽  
Pedestrian Navigation

This paper presents an evaluation of real-time kinematic (RTK)/Pseudolite/landmarks assistance heuristic drift elimination (LAHDE)/inertial measurement unit-based personal dead reckoning systems (IMU-PDR) integrated pedestrian navigation system for urban and indoor environments. Real-time kinematic (RTK) technique is widely used for high-precision positioning and can provide periodic correction to inertial measurement unit (IMU)-based personal dead reckoning systems (PDR) outdoors. However, indoors, where global positioning system (GPS) signals are not available, RTK fails to achieve high-precision positioning. Pseudolite can provide satellite-like navigation signals for user receivers to achieve positioning in indoor environments. However, there are some problems in pseudolite positioning field, such as complex multipath effect in indoor environments and integer ambiguity of carrier phase. In order to avoid the limitation of these factors, a local search method based on carrier phase difference with the assistance of IMU-PDR is proposed in this paper, which can achieve higher positioning accuracy. Besides, heuristic drift elimination algorithm with the assistance of manmade landmarks (LAHDE) is introduced to eliminate the accumulated error in headings derived by IMU-PDR in indoor corridors. An algorithm verification system was developed to carry out real experiments in a cooperation scene. Results show that, although the proposed pedestrian navigation system has to use human behavior to switch the positioning algorithm according to different scenarios, it is still effective in controlling the IMU-PDR drift error in multiscenarios including outdoor, indoor corridor, and indoor room for different people.

The Automatic Basketball Rebound System

2018 ◽  
Author(s):  
Thomas Smith ◽  
Vidya K. Nandikolla
Keyword(s):  
Inertial Measurement Unit ◽  
High Efficiency ◽  
Dead Reckoning ◽  
Measurement Unit ◽  
System Response ◽  
The Novel ◽  
Rotary Table ◽  
Inertial Measurement ◽  
Correct Orientation ◽  
High Level

In the sport of basketball, it is important to practice shooting the ball to develop the skill of making the shot in the basket at a high efficiency. Making shots at a high efficiency allows the player to succeed at a high level in the sport. The main focus of the paper describes the design and development of an automatic basketball rebound (ABR) system. The developed ABR provides a system that will launch the ball back to the player at any position on the court within a 50-foot radius. This is accomplished by a variable spring loaded launching mechanism that will compress a spring, depending on the players location, to generate the appropriate force required to launch the ball back to the player. The novel launching mechanism developed is mounted to a rotary table that ensures the launching mechanism is in the correct orientation with the player once the ball is launched. The player is outfitted with an inertial measurement unit to track their position using a method known as dead reckoning. This information is relayed back to a microcontroller that determines the system response. The ABR system is made from lightweight materials and is compact such that it is easy to move around compared to its predecessors.

scholarly journals Real-Time Gait Analysis Using a Single Head-Worn Inertial Measurement Unit

2018 ◽  
Vol 64 (2) ◽  
pp. 240-248 ◽  
Author(s):  
Tong-Hun Hwang ◽  
Julia Reh ◽  
Alfred O. Effenberg ◽  
Holger Blume
Keyword(s):  
Gait Analysis ◽  
Real Time ◽  
Inertial Measurement Unit ◽  
Measurement Unit ◽  
Inertial Measurement

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

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4782 ◽  
Author(s):  
Yong Hun Kim ◽  
Min Jun Choi ◽  
Eung Ju Kim ◽  
Jin Woo Song
Keyword(s):  
Magnetic Field ◽  
Importance Sampling ◽  
Dead Reckoning ◽  
Position Error ◽  
Magnetic Sensors ◽  
Map Matching ◽  
Multiple Sensors ◽  
Pedestrian Navigation ◽  
Zero Velocity ◽  
Pedestrian Dead Reckoning

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.

Study of Zero Velocity Update for Both Low- and High-Speed Human Activities

2011 ◽  
Vol 2 (2) ◽  
pp. 46-67 ◽  
Author(s):  
R. Zhang ◽  
M. Loschonsky ◽  
L.M. Reindl
Keyword(s):  
Human Activities ◽  
Inertial Measurement Unit ◽  
High Speed ◽  
Inertial Sensor ◽  
Stair Climbing ◽  
Measurement Unit ◽  
Test Cases ◽  
Zero Velocity ◽  
Wide Range ◽  
Algorithm Implementation

Previous studies show that inertial sensor-based personal positioning benefited from Zero Velocity Update (ZUPT) method by resetting the foot speed at every foot step. However, only the solution for normal pedestrian movement with small velocity like walking was given. This paper presents a novel ZUPT system which can be used in a wide range of human activities, including walking, running, and stair climbing by using two inertial measurement unit (IMU) modules. One is attached on the centre of the human body for human activities’ classification and recognition. The other one is mounted on the foot for ZUPT algorithm implementation based on the result of activities’ recognition. Test cases include stair climbing by walking and running, walking, fast walking, and running. In all cases, most of the steps are able to be detected and the new ZUPT system can be successfully implemented.

scholarly journals Real-Time Parallel-Serial LiDAR-Based Localization Algorithm with Centimeter Accuracy for GPS-Denied Environments

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7123
Author(s):  
Jakub Niedzwiedzki ◽  
Adam Niewola ◽  
Piotr Lipinski ◽  
Piotr Swaczyna ◽  
Aleksander Bobinski ◽  
...  
Keyword(s):  
Real Time ◽  
Inertial Measurement Unit ◽  
Gaussian Mixture ◽  
Measurement Unit ◽  
Localization Algorithm ◽  
Parallel Data ◽  
Robot Positioning ◽  
Experimental Findings ◽  
Serial Algorithm ◽  
Fusion Approach

In this paper, we introduce a real-time parallel-serial algorithm for autonomous robot positioning for GPS-denied, dark environments, such as caves and mine galleries. To achieve a good complexity-accuracy trade-off, we fuse data from light detection and ranging (LiDAR) and an inertial measurement unit (IMU). The proposed algorithm’s main novelty is that, unlike in most algorithms, we apply an extended Kalman filter (EKF) to each LiDAR scan point and calculate the location relative to a triangular mesh. We also introduce three implementations of the algorithm: serial, parallel, and parallel-serial. The first implementation verifies the correctness of our innovative approach, but is too slow for real-time execution. The second approach implements a well-known parallel data fusion approach, but is still too slow for our application. The third and final implementation of the presented algorithm along with the state-of-the-art GPU data structures achieves real-time performance. According to our experimental findings, our algorithm outperforms the reference Gaussian mixture model (GMM) localization algorithm in terms of accuracy by a factor of two.

Sign in / Sign up

Export Citation Format

Share Document