Indoor Pedestrian Positioning Method Based on Pelvic Motion Model Using Inertial Sensors

2020 ◽  
Author(s):  
Miao Yang ◽  
Lelai Zhou ◽  
Yibin Li
Keyword(s):  
Inertial Sensors ◽  
Motion Model ◽  
Positioning Method ◽  
Pelvic Motion

scholarly journals Research on the Shearer Positioning Method Based on the MEMS Inertial Sensors/Odometer Integrated Navigation System and RTS Smoother

Micromachines ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1527
Author(s):  
Jiangtao Zheng ◽  
Sihai Li ◽  
Shiming Liu ◽  
Bofan Guan ◽  
Dong Wei ◽  
...  
Keyword(s):  
Inertial Measurement Unit ◽  
Inertial Sensors ◽  
Optimal Estimation ◽  
Measurement Unit ◽  
Integrated Navigation ◽  
Estimation Model ◽  
Positioning Accuracy ◽  
Inertial Measurement ◽  
Integrated Navigation System ◽  
Positioning Method

The shearer positioning method with an inertial measurement unit and the odometer is feasible in the longwall coal-mining process. However, the positioning accuracy will continue to decrease, especially for the micro-electromechanical inertial measurement unit (MIMU). In order to further improve the positioning accuracy of the shearer without adding other external sensors, the positioning method of the Rauch-Tung-Striebel (RTS) smoother-aided MIMU and odometer is proposed. A Kalman filter (KF) with the velocity and position measurements, which are provided by the odometer and closing path optimal estimation model (CPOEM), respectively, is established. The observability analysis is discussed to study the possible conditions under which the error states of KF can be estimated. A RTS smoother with the above-mentioned KF as the forward filter is built. Finally, the experiments of simulating the movement of the shearer through a mobile carrier were carried out, with a longitudinal movement distance of 44.6 m and a lateral advance distance of 1.2 m. The results show that the proposed method can effectively improve the positioning accuracy. In addition, the odometer scale factor and mounting angles can be estimated in real time.

scholarly journals Multi-Sensor Fusion Positioning Method Based on Batch Inverse Covariance Intersection and IMM

2021 ◽  
Vol 11 (11) ◽  
pp. 4908
Author(s):  
Yanxu Liu ◽  
Zhongliang Deng ◽  
Enwen Hu
Keyword(s):  
Sensor Fusion ◽  
Estimation Error ◽  
Error Variance ◽  
Multiple Models ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Motion Model ◽  
Positioning Accuracy ◽  
Covariance Intersection ◽  
Positioning Method

For mass application positioning demands, the current single positioning sensor cannot provide reliable and accurate positioning. Herein, we present batch inverse covariance intersection (BICI) and BICI with interacting multiple model (BICI-IMM) multi-sensor fusion positioning methods, which are based on the batch form of the sequential inverse covariance intersection (SICI) fusion method. Meanwhile, it is proved that the BICI is robust. Compared with SICI, BICI-IMM reduces estimation error variance of the motion model and has less conservativeness. The BICI-IMM algorithm improves the accuracy of local filtering by interacting with multiple models and realizes global fusion estimation based on BICI. The validity of the BICI and BICI-IMM algorithm are demonstrated by two simulations and experiments in the open and semi-open scenes, and its positioning accuracy relations are shown. In addition, it is demonstrated that the BICI-IMM algorithm can improve the positioning accuracy in the actual scenes.

Vehicle Localization Based on Global Navigation Satellite System Aided by Three-Dimensional Map

2017 ◽  
Vol 2621 (1) ◽  
pp. 55-61 ◽  
Author(s):  
Yanlei Gu ◽  
Li-Ta Hsu ◽  
Shunsuke Kamijo
Keyword(s):  
Inertial Sensors ◽  
Three Dimensional ◽  
Satellite System ◽  
Navigation Satellite ◽  
Positioning Accuracy ◽  
Vehicle Localization ◽  
Localization System ◽  
Gnss Positioning ◽  
Positioning Method ◽  
Global Navigation Satellite

Accurate vehicle localization technologies are significant for current onboard navigation systems and future autonomous vehicles. More specifically, positioning accuracy is expected at the submeter level. This paper presents an accurate vehicle self-localization system and evaluates the proposed system in different classes of urban environments. The developed system adopts an innovative global navigation satellite system (GNSS) positioning method as the key technique. The GNSS positioning method can improve the positioning error by reducing the effects of multipath interference and non-line-of-sight errors with the aid of a three-dimensional map. To improve positioning accuracy further, the vehicle localization system integrates the GNSS positioning technique with inertial sensors and vision sensors by considering the characteristics of each sensor. The inertial sensors represent vehicle movement with heading direction and vehicle speed. The vision sensor is used to recognize the position change relative to lane markings on the road surface. Those techniques and sensors collaborate to provide an accurate position in the global coordinate system. To verify the effectiveness and stability of the proposed system, a series of tests was conducted in one of the most challenging urban cities, Tokyo. The experiment results demonstrate that the proposed system can achieve submeter accuracy for the positioning error mean and has a 90% correct lane rate in the localization.

scholarly journals A Universal Simulation Framework of Shipborne Inertial Sensors Based on the Ship Motion Model and Robot Operating System

2021 ◽  
Vol 9 (8) ◽  
pp. 900
Author(s):  
Qianfeng Jing ◽  
Haichao Wang ◽  
Bin Hu ◽  
Xiuwen Liu ◽  
Yong Yin
Keyword(s):  
Operating System ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Measurement Data ◽  
Ship Motion ◽  
Motion Model ◽  
Simulation Framework ◽  
Test Environment ◽  
Inertial Measurement ◽  
Robot Operating System

A complete virtual test environment is a powerful tool for Autonomous Surface Vessels (ASVs) research, and the simulation of ship motion and shipborne sensors is one of the prerequisites for constructing such an environment. This paper proposed a universal simulation framework of shipborne inertial sensors. A ship motion model considering environmental disturbances is proposed to simulate the six-degrees-of-freedom motion of ships. The discrete form of the inertial sensor stochastic error model is derived. The inertial measurement data are simulated by adding artificial errors to a simulated motion status. In addition, the ship motion simulation, inertial measurement simulation, and environment simulation nodes are implemented based on the computational graph architecture of the Robot Operating System (ROS). The benefit from the versatility of the ROS messages, the format of simulated inertial measurement is exactly the same as that of real sensors, which provides a research basis for the fusion perception algorithm based on visual–inertial and laser–inertial sensors in the research field of ASVs.

scholarly journals A novel motion-model-free UWB short-range positioning method

2019 ◽  
Vol 14 (4) ◽  
pp. 815-820
Author(s):  
Navid Ayoobi ◽  
Mohammad Ghavami ◽  
Amir Masoud Rabiei
Keyword(s):  
Indoor Environment ◽  
Channel Model ◽  
Real Data ◽  
Location Based Services ◽  
Outdoor Environment ◽  
Motion Model ◽  
Floor Plan ◽  
Positioning Systems ◽  
Model Free ◽  
Positioning Method

AbstractIn recent years, the number of location-based services is increasing and consequently, the researchers’ attentions are captivated in designing accurate real-time positioning systems. Despite having a good performance in outdoor environment, global positioning system is not capable of estimating an object’s position in an indoor environment precisely. In this paper, we present a novel tracking algorithm for indoor environment with a known floor plan. The object location is estimated by utilizing the information of the multipath components which are created by one physical and some virtual anchors. We will link this information to the floor plan by defining a channel model that has a combination of stochastic and deterministic traits. As we have used only one physical anchor in this paper, we would encounter several challenges such as lack of data association and existence of clutters amid real data. We dealt with these problems through random finite set methodology. Additionally, we will demonstrate that the proposed method is not restricted by the model of the motion and is capable to precisely track the trajectory. It will be shown that it provides a better accuracy, particularly in nonlinear trajectories, compared with two other relevant models which are adopting linear motion model.

Sign in / Sign up

Export Citation Format

Share Document