scholarly journals Indoor and Outdoor Low-Cost Seamless Integrated Navigation System Based on the Integration of INS/GNSS/LIDAR System

2020 ◽  
Vol 12 (19) ◽  
pp. 3271
Author(s):  
Ningbo Li ◽  
Lianwu Guan ◽  
Yanbin Gao ◽  
Shitong Du ◽  
Menghao Wu ◽  
...  
Keyword(s):  
Navigation System ◽  
Low Cost ◽  
Dead Reckoning ◽  
Satellite System ◽  
Outdoor Environment ◽  
Mode Switching ◽  
Integrated Navigation ◽  
Switching Algorithm ◽  
Navigation Algorithm ◽  
Indoor And Outdoor

Global Navigation Satellite System (GNSS) provides accurate positioning data for vehicular navigation in open outdoor environment. In an indoor environment, Light Detection and Ranging (LIDAR) Simultaneous Localization and Mapping (SLAM) establishes a two-dimensional map and provides positioning data. However, LIDAR can only provide relative positioning data and it cannot directly provide the latitude and longitude of the current position. As a consequence, GNSS/Inertial Navigation System (INS) integrated navigation could be employed in outdoors, while the indoors part makes use of INS/LIDAR integrated navigation and the corresponding switching navigation will make the indoor and outdoor positioning consistent. In addition, when the vehicle enters the garage, the GNSS signal will be blurred for a while and then disappeared. Ambiguous GNSS satellite signals will lead to the continuous distortion or overall drift of the positioning trajectory in the indoor condition. Therefore, an INS/LIDAR seamless integrated navigation algorithm and a switching algorithm based on vehicle navigation system are designed. According to the experimental data, the positioning accuracy of the INS/LIDAR navigation algorithm in the simulated environmental experiment is 50% higher than that of the Dead Reckoning (DR) algorithm. Besides, the switching algorithm developed based on the INS/LIDAR integrated navigation algorithm can achieve 80% success rate in navigation mode switching.

scholarly journals A Low-Cost Underground Garage Navigation Switching Algorithm Based on Kalman Filtering

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1861 ◽  
Author(s):  
Ningbo Li ◽  
Yanbin Gao ◽  
Ye Wang ◽  
Zhejun Liu ◽  
Lianwu Guan ◽  
...  
Keyword(s):  
Low Cost ◽  
Dead Reckoning ◽  
Fast Response ◽  
Outdoor Environment ◽  
Vehicle Navigation ◽  
Initial State ◽  
Parking Lots ◽  
Switching Algorithm ◽  
Indoor And Outdoor Environments ◽  
Indoor And Outdoor

Modern parking lots have gradually developed into underground garages to improve the efficient use of space. However, the complex design of parking lots also increases the demands on vehicle navigation. The traditional method of navigation switching only uses satellite signals. After the Position Dilution Of Precision (PDOP) of satellite signals is over the limit, vehicle navigation will enter indoor mode. It is not suitable for vehicles in underground garages to switch modes with a fast-response system. Therefore, this paper chooses satellite navigation, inertial navigation, and the car system to combine navigation. With the condition that the vehicle can freely travel through indoor and outdoor environments, high-precision outdoor environment navigation is used to provide the initial state of underground navigation. The position of the vehicle underground is calculated by the Dead Reckoning (DR) navigation system. This paper takes advantage of the Extended Kalman Filter (EKF) algorithm to provide two freely switchable navigation modes for vehicles in ground and underground garages. The continuity, robustness, fast response, and low cost of the indoor and outdoor switching navigation methods are verified in real-time systems.

scholarly journals Moving Target Positioning Based on a Distributed Camera Network

2014 ◽  
Vol 2014 ◽  
pp. 1-11
Author(s):  
Long Zhao ◽  
Zhen Liu ◽  
Tiejun Li ◽  
Baoqi Huang ◽  
Lihua Xie
Keyword(s):  
Low Cost ◽  
Satellite System ◽  
Outdoor Environment ◽  
Moving Target ◽  
Camera Network ◽  
World Coordinate System ◽  
Outdoor Environments ◽  
Global Navigation Satellite ◽  
Systematic Framework ◽  
Indoor And Outdoor

We propose a systematic framework for moving target positioning based on a distributed camera network. In the proposed framework, low-cost static cameras are deployed to cover a large region, moving targets are detected and then tracked using corresponding algorithms, target positions are estimated by making use of the geometrical relationships among those cameras after calibrating those cameras, and finally, for each target, its position estimates obtained from different cameras are unified into the world coordinate system. This system can function as complementary positioning information sources to realize moving target positioning in indoor or outdoor environments when global navigation satellite system (GNSS) signals are unavailable. The experiments are carried out using practical indoor and outdoor environment data, and the experimental results show that the systematic framework and inclusive algorithms are both effective and efficient.

scholarly journals GPS/INS Integrated Navigation System for Autonomous Robots

2020 ◽  
Vol 3 (SI1) ◽  
pp. First
Author(s):  
Tran Ngoc Huy ◽  
Le Manh Cam ◽  
Nguyen Thanh Nam
Keyword(s):  
Navigation System ◽  
Autonomous Robots ◽  
Low Cost ◽  
Dead Reckoning ◽  
Experimental System ◽  
Velocity Estimation ◽  
Integrated System ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Short Period

Nowadays, autonomous robots are capable of replacing people with hard work or in dangerous environments, so this field is rapidly developing. One of the most important tasks in controlling these robots is to determine its current position. The Global Positioning System (GPS) was originally developed for military purposes but is now widely used for civilian purposes such as mapping, navigation for land vehicles, marine, etc. However, GPS has some disadvantages, like the update rate is low or sometimes the satellites’ signal is suspended. Another navigation system is the Inertial Navigation System (INS) can allow you to determine position, velocities, and attitude from the subject’s status, like acceleration and rotation rate. Essentially, INS is a dead-reckoning system, so it has a huge cumulative error. An effective method is to integrate GPS with INS, in which the center is a nonlinear estimator (e.g., the Extended Kalman filter) to determine the navigation error, from which it can update the position the object more accurately. To improve even better accuracy, this paper proposes a new method that combines the original integrated GPS/INS with tri-axis rotation angles estimation and velocity constraints. The experimental system is built on a low-cost IMU with a tri-axis gyroscope, accelerometer, and magnetometer, and a GPS module to verify the model algorithm. Experiment results have shown that the rotation angles estimator helps us to determine the Euler angles correctly, thereby increasing the quality of the position and velocity estimation. In practice, the accuracy of the roll and pitch angle is 2 degrees; the error of the yaw angle is still large. The achieved horizontal accuracy is 2m when the GPS signal is stable and 3m when the GPS signal is lost in a short period. Compared with individual GPS, the error of the integrated system is about 10% smaller.

Research on outdoor AGV precise navigation based on BDS/INS data fusion

2021 ◽  
pp. 1-12
Author(s):  
Yongwei Tang ◽  
Huijuan Hao ◽  
Jun Zhou ◽  
Yuexiang Lin ◽  
Zhenzhen Dong
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Navigation System ◽  
Integrated Navigation ◽  
Complex Environments ◽  
Assisted Navigation ◽  
Precise Control ◽  
Bee Colony ◽  
Navigation Algorithm ◽  
Navigation Error

AGV (Automated Guided Vehicle) technology has attracted increasing attention. Precise control of AGV position and attitude information in complex operating environment is a key part of smart factories. With outdoor AGV as a platform, this study uses BDS/INS combined navigation system combining Beidou positioning system and inertial navigation system and takes the velocity and position difference between BDS and INS as a model. An integrated navigation method is proposed to improve bee colony algorithm and optimize the BP neural network-assisted Kalman filtering to achieve accurate positioning. Moreover, the optimization of BP neural network navigation using INS navigation, network-assisted navigation and bee colony algorithm is simulated. Results demonstrate that the integrated navigation algorithm has effectiveness and feasibility, and can solve the problems of BDS misalignment and large INS navigation error in complex environments.

scholarly journals MEMS Based SINS/OD Filter for Land Vehicles’ Applications

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Huisheng Liu ◽  
Zengcai Wang ◽  
Susu Fang ◽  
Chao Li
Keyword(s):  
Navigation System ◽  
Velocity Measurement ◽  
Microelectromechanical Systems ◽  
Low Cost ◽  
Detection Algorithm ◽  
Measurement Unit ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Land Vehicles ◽  
Mems Imu

A constrained low-cost SINS/OD filter aided with magnetometer is proposed in this paper. The filter is designed to provide a land vehicle navigation solution by fusing the measurements of the microelectromechanical systems based inertial measurement unit (MEMS IMU), the magnetometer (MAG), and the velocity measurement from odometer (OD). First, accelerometer and magnetometer integrated algorithm is studied to stabilize the attitude angle. Next, a SINS/OD/MAG integrated navigation system is designed and simulated, using an adaptive Kalman filter (AKF). It is shown that the accuracy of the integrated navigation system will be implemented to some extent. The field-test shows that the azimuth misalignment angle will diminish to less than 1°. Finally, an outliers detection algorithm is studied to estimate the velocity measurement bias of the odometer. The experimental results show the enhancement in restraining observation outliers that improves the precision of the integrated navigation system.

Design of a low-cost attitude determination GPS/INS integrated navigation system

GPS Solutions ◽  
2005 ◽  
Vol 9 (4) ◽  
pp. 294-311 ◽  
Author(s):  
Dong-Hwan Hwang ◽  
Sang Heon Oh ◽  
Sang Jeong Lee ◽  
Chansik Park ◽  
Chris Rizos
Keyword(s):  
Navigation System ◽  
Attitude Determination ◽  
Low Cost ◽  
Integrated Navigation ◽  
Integrated Navigation System

Studies of the integrated navigation system correction in the absence of a satellite signal

2021 ◽  
Author(s):  
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Operation Mode ◽  
Satellite System ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Correction Signal ◽  
Satellite Signal ◽  
Navigation Information

The schemes of navigation systems correction are considered. The operation mode of the aircraft during navigation is analyzed. An adaptive modification of the linear Kalman filter is used to correct the navigation information. An algorithm for predicting a correction signal based on a neural network in the event of a loss of a SNS correction signal is formed. Experimental results show the effectiveness of the algorithm. Keywords aircraft; inertial navigation system; satellite system; Kalman filter; neural networks; genetic algorithm

Sign in / Sign up

Export Citation Format

Share Document