An enhanced inertial navigation system based on a low-cost IMU and laser scanner

2012 ◽  
Author(s):  
Hyung-Soon Kim ◽  
Seung-Ho Baeg ◽  
Kwang-Woong Yang ◽  
Kuk Cho ◽  
Sangdeok Park
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Low Cost ◽  
Laser Scanner ◽  
Inertial Navigation

MODELING AND NUMERICAL SIMULATION OF AN ALGORITHM FOR THE INERTIAL SENSORS ERRORS REDUCTION AND FOR THE INCREASE OF THE STRAP-DOWN NAVIGATOR REDUNDANCY DEGREE IN A LOW COST ARCHITECTURE

2010 ◽  
Vol 34 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Lucian T. Grigorie ◽  
Ruxandra M. Botez
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Lower Cost ◽  
Inertial Sensors ◽  
Low Cost ◽  
Inertial Navigation ◽  
Noise Power ◽  
Integration Algorithm ◽  
The Cost ◽  
Miniaturized Sensors

In this paper, an algorithm for the inertial sensors errors reduction in a strap-down inertial navigation system, using several miniaturized inertial sensors for each axis of the vehicle frame, is conceived. The algorithm is based on the idea of the maximum ratio-combined telecommunications method. We consider that it would be much more advantageous to set a high number of miniaturized sensors on each input axis of the strap-down inertial system instead of a single one, more accurate but expensive and with larger dimensions. Moreover, a redundant system, which would isolate any of the sensors in case of its malfunctioning, is obtained. In order to test the algorithm, Simulink code is used for algorithm and for the acceleration inertial sensors modeling. The Simulink resulted sensors models include their real errors, based on the data sheets parameters, and were conceived based on the IEEE analytical standardized accelerometers model. An integration algorithm is obtained, in which the signal noise power delivered to the navigation processor, is reduced, proportionally with the number of the integrated sensors. At the same time, the bias of the resulted signal is reduced, and provides a high redundancy degree for the strap-down inertial navigation system at a lower cost than at the cost of more accurate and expensive sensors.

Error Modelling and Optimal Estimation of Laser Scanning Aided Inertial Navigation System in GNSS-Denied Environments

2018 ◽  
Vol 72 (3) ◽  
pp. 741-758 ◽  
Author(s):  
W.I. Liu ◽  
Zhixiong Li ◽  
Zhichao Zhang
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Laser Scanning ◽  
Inertial Navigation System ◽  
Laser Scanner ◽  
Inertial Navigation ◽  
Integration Algorithm ◽  
Biased Estimation ◽  
State Estimations ◽  
Estimation And Filtering

A Laser Scanning aided Inertial Navigation System (LSINS) is able to provide highly accurate position and attitude information by aggregating laser scanning and inertial measurements under the assumption that the rigid transformation between sensors is known. However, a LSINS is inevitably subject to biased estimation and filtering divergence errors due to inconsistent state estimations between the inertial measurement unit and the laser scanner. To bridge this gap, this paper presents a novel integration algorithm for LSINS to reduce the inconsistences between different sensors. In this new integration algorithm, the Radial Basis Function Neural Networks (RBFNN) and Singular Value Decomposition Unscented Kalman Filter (SVDUKF) are used together to avoid inconsistent state estimations. Optimal error estimation in the LSINS integration process is achieved to reduce the biased estimation and filtering divergence errors through the error state and measurement error model built by the proposed method. Experimental tests were conducted to evaluate the navigation performance of the proposed method in Global Navigation Satellite System (GNSS)-denied environments. The navigation results demonstrate that the relationship between the laser scanner coordinates and the inertial sensor coordinates can be established to reduce sensor measurement inconsistencies, and LSINS position accuracy can be improved by 23·6% using the proposed integration method compared with the popular Extended Kalman Filter (EKF) algorithm.

scholarly journals The Development of a Low Cost Inertial Navigation System for a Remotely Operated Vehicle

2021 ◽  
Author(s):  
◽  
Jason Dean Edwards
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Low Cost ◽  
Inertial Navigation ◽  
Electronic Systems ◽  
Remotely Operated Vehicle ◽  
Underwater Environment ◽  
Laser Scanners ◽  
System Use ◽  
Robotic Vehicles

<p>Modern robotic vehicles use a large and varied set of sensors to navigate and localise their position in the environment and determine where they should be heading to accomplish their tasks. These sensors include GPS, infrared and ultrasonic range finders, laser scanners and sonar. However, the underwater environment presents challengers for modern robotic vehicles because most sensors that are typically used for navigation and localisation have reduced or no functionality underwater. This thesis details the design and construction of a low cost Inertial Navigation System use on the Victoria University of Wellington's (VUW) Mechatronics group Remotely Operated Vehicle (ROV). The major electronic systems, comprising of the onboard computer and microcontroller, of the ROV have been upgraded to allow for the increased computational power that the Inertial Navigation System needs and to allow further upgrading and installation of electrical and electronic systems in the vehicle as they are required. Modifications to the chassis allow quick and simple disassembly of the ROV to repair or replace major components if the need arises.</p>

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