scholarly journals An Indoor Navigation Algorithm Using Multi-Dimensional Euclidean Distance and an Adaptive Particle Filter

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8228
Author(s):  
Yunbing Hu ◽  
Ao Peng ◽  
Biyu Tang ◽  
Hongying Xu
Keyword(s):  
Particle Filter ◽  
Navigation System ◽  
Euclidean Distance ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Indoor Navigation ◽  
Fingerprint Matching ◽  
Positioning Accuracy ◽  
Cumulative Error ◽  
Wifi Fingerprint

The inertial navigation system has high short-term positioning accuracy but features cumulative error. Although no cumulative error occurs in WiFi fingerprint localization, mismatching is common. A popular technique thus involves integrating an inertial navigation system with WiFi fingerprint matching. The particle filter uses dead reckoning as the state transfer equation and the difference between inertial navigation and WiFi fingerprint matching as the observation equation. Floor map information is introduced to detect whether particles cross the wall; if so, the weight is set to zero. For particles that do not cross the wall, considering the distance between current and historical particles, an adaptive particle filter is proposed. The adaptive factor increases the weight of highly trusted particles and reduces the weight of less trusted particles. This paper also proposes a multidimensional Euclidean distance algorithm to reduce WiFi fingerprint mismatching. Experimental results indicate that the proposed algorithm achieves high positioning accuracy.

scholarly journals Positioning Accuracy of the Shearer Based on a Strapdown Inertial Navigation System in Underground Coal Mining

2020 ◽  
Vol 10 (6) ◽  
pp. 2176
Author(s):  
Gang Wu ◽  
Xinqiu Fang ◽  
Lei Zhang ◽  
Minfu Liang ◽  
Jiakun Lv ◽  
...  
Keyword(s):  
Coal Mining ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Strapdown Inertial Navigation System ◽  
Experimental Results ◽  
Positioning System ◽  
Zero Bias ◽  
Positioning Accuracy ◽  
Strapdown Inertial Navigation

Automation and intelligent coal mining comprise the most important fields in coal mining technology research. The key to automation and intelligent coal mining is the automated mining of the working face, and accurate positioning of the shearer is one of the most important technologies in the automated mining process. However, significant defects in non-inertial navigation system (INS)-based methods have led to low positioning accuracy. In this paper, we propose a new shearer positioning technology to further improve the positioning accuracy of the shearer and monitor the shearer position in real time. The shearer positioning system proposed is based on the strapdown inertial navigation system (SINS). We conducted shearer positioning experiments with gyroscopes, accelerometers, and other inertial navigation instruments. The experimental results are thoroughly studied on the basis of error compensation techniques such as inertial instrument zero bias compensation and Kalman filter compensation. Compared with traditional shearer positioning technology, the experimental results show that the shearer positioning system based on SINS can achieve more accurate positioning of the shearer and can accurately reflect the running characteristics of the shearer working the mining face.

scholarly journals Robust Visual-Inertial Navigation System for Low Precision Sensors under Indoor and Outdoor Environments

2021 ◽  
Vol 13 (4) ◽  
pp. 772
Author(s):  
Changhui Xu ◽  
Zhenbin Liu ◽  
Zengke Li
Keyword(s):  
Optical Flow ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Low Cost ◽  
Inertial Navigation ◽  
Point Method ◽  
Feature Point ◽  
Positioning Accuracy ◽  
Flow Method ◽  
Optical Flow Method

Simultaneous Localization and Mapping (SLAM) has always been the focus of the robot navigation for many decades and becomes a research hotspot in recent years. Because a SLAM system based on vision sensor is vulnerable to environment illumination and texture, the problem of initial scale ambiguity still exists in a monocular SLAM system. The fusion of a monocular camera and an inertial measurement unit (IMU) can effectively solve the scale blur problem, improve the robustness of the system, and achieve higher positioning accuracy. Based on a monocular visual-inertial navigation system (VINS-mono), a state-of-the-art fusion performance of monocular vision and IMU, this paper designs a new initialization scheme that can calculate the acceleration bias as a variable during the initialization process so that it can be applied to low-cost IMU sensors. Besides, in order to obtain better initialization accuracy, visual matching positioning method based on feature point is used to assist the initialization process. After the initialization process, it switches to optical flow tracking visual positioning mode to reduce the calculation complexity. By using the proposed method, the advantages of feature point method and optical flow method can be fused. This paper, the first one to use both the feature point method and optical flow method, has better performance in the comprehensive performance of positioning accuracy and robustness under the low-cost sensors. Through experiments conducted with the EuRoc dataset and campus environment, the results show that the initial values obtained through the initialization process can be efficiently used for launching nonlinear visual-inertial state estimator and positioning accuracy of the improved VINS-mono has been improved by about 10% than VINS-mono.

scholarly journals Mobile robot positioning algorithm based on Kalman filtering method in network environment

2020 ◽  
Vol 327 ◽  
pp. 03005
Author(s):  
Shuang Zhang
Keyword(s):  
Mobile Robot ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Measurement Unit ◽  
Relative Positioning ◽  
Positioning Accuracy ◽  
Reference Information ◽  
Absolute Positioning ◽  
Positioning Method

Positioning is the basic link in a multi-mobile robot control system, and is also a problem that must be solved before completing a specified task. The positioning method can be generally divided into relative positioning and absolute positioning. Absolute positioning method refers to that the robot calculates its current position by acquiring the reference information of some known positions in the outside world, calculating the relationship between itself and the reference information. Absolute positioning generally adopts methods based on beacons, environment map matching, and visual positioning. The relative positioning method mainly uses the inertial navigation system INS. The inertial navigation system directly fixes the inertial measurement unit composed of the gyroscope and the accelerometer to the target device, and uses the inertial devices such as the gyroscope and the accelerometer to measure the triaxial angular velocity and The three-axis acceleration information is measured and integrated, and the mobile robot coordinates are updated in real time. Combined with the initial inertial information of the target device, navigation information such as the attitude, speed, and position of the target device is obtained through integral operation [1-2]. The inertial navigation system does not depend on external information when it is working, and is not easily damaged by interference. As an autonomous navigation system, it has the advantages of high data update rate and high short-term positioning accuracy [3]. However, under the long-term operation of inertial navigation, due to the cumulative error of integration, the positioning accuracy is seriously degraded, so it is necessary to seek an external positioning method to correct its position information [4]

Kalman Filter-Based Sensor Fusion for Improving Localization of AGV

2012 ◽  
Vol 488-489 ◽  
pp. 1818-1822 ◽  
Author(s):  
In Seong Lee ◽  
Jae Yong Kim ◽  
Jun Ha Lee ◽  
Jung Min Kim ◽  
Sung Sin Kim
Keyword(s):  
Kalman Filter ◽  
Sensor Fusion ◽  
High Precision ◽  
Navigation System ◽  
High Speed ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Positioning Accuracy ◽  
Global Positioning ◽  
Precision Device

This paper proposes localization using sensor fusion with a laser navigation and an inertial navigation system for indoor mobile. The laser navigation is a device that measures angle and distance between the robot and the reflectors. Although it is the high-precision device for indoor global positioning, there is a problem that the accuracy of laser navigation significantly drops while moving at high speed and rapid turning. To solve this problem, the laser navigation was fused to inertial navigation system through Kalman filter. For experiment, we use omnidirectional robot with Mecanum Wheels and analyze the positioning accuracy according to driving direction of the robot.

Investigation of the positioning accuracy for the integrated navigation system at the measurement signals interruption

2021 ◽  
Author(s):  
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Forecast Accuracy ◽  
Satellite Navigation ◽  
Integrated Navigation ◽  
Positioning Accuracy ◽  
Satellite Navigation System ◽  
Integrated Navigation System ◽  
Mobile Carrier

An integrated navigation system as part of an inertial navigation system corrected by signals from a satellite navigation system is researched. The integrated navigation system is installed on a mobile carrier is arranged. The organization of the experimental study, the design of the stand used to install the equipment on a mobile carrier, and the measurement processing technique are considered. When the signals of the satellite system disappear, signal prediction algorithms are used. The results of assessing the positioning accuracy of the integrated navigation system in case of discontinuities in the reception of navigation signals, assessing the forecast accuracy by using the presented algorithms and conclusions drawn from the analysis of the results are presented. Keywords inertial navigation system; satellite navigation system; predictive model; positioning accuracy; trends; self-organization; identification

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