Development of a laser scanner-based navigation system for a combine harvester

2014 ◽  
Vol 7 (1) ◽  
pp. 7-13 ◽  
Author(s):  
Jongmin Choi ◽  
Xiang Yin ◽  
Liangliang Yang ◽  
Noboru Noguchi
Keyword(s):  
Navigation System ◽  
Laser Scanner ◽  
Combine Harvester

scholarly journals Development of a Laser Scanner-based Navigation System for a Combine Harvester

2013 ◽  
Vol 46 (18) ◽  
pp. 103-108 ◽  
Author(s):  
Jongmin CHOI ◽  
Xiang Yin ◽  
Noboru Noguchi
Keyword(s):  
Navigation System ◽  
Laser Scanner ◽  
Combine Harvester

Accuracy improvement in 3D laser scanner based on dynamic triangulation for autonomous navigation system

2017 ◽  
Author(s):  
Oscar Real-Moreno ◽  
Julio C. Rodriguez-Quinonez ◽  
Oleg Sergiyenko ◽  
Luis C. Basaca-Preciado ◽  
Daniel Hernandez-Balbuena ◽  
...  
Keyword(s):  
Navigation System ◽  
Autonomous Navigation ◽  
Laser Scanner ◽  
Accuracy Improvement ◽  
Autonomous Navigation System

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.

Tracing algorithm and control strategy for crawler rice combine harvester auxiliary navigation system

2021 ◽  
Vol 211 ◽  
pp. 50-62
Author(s):  
Zhuohuai Guan ◽  
Ying Li ◽  
Senlin Mu ◽  
Min Zhang ◽  
Tao Jiang ◽  
...  
Keyword(s):  
Control Strategy ◽  
Navigation System ◽  
Combine Harvester ◽  
Tracing Algorithm ◽  
And Control

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
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