Abstract
The iterative closest contour point (ICCP) matching algorithm has become more and more widely used in the underwater geomagnetic aided inertial navigation system (INS). In practical application, the traditional ICCP algorithm is sensitive to the initial positioning error of the INS and can only do rigid transformation for the INS track of the vehicle. Particularly when there exists scale error, the accuracy and stability of the traditional ICCP algorithm will be affected. To solve this problem, an improved algorithm based on affine transformation is proposed. Firstly, the fundamental of the ICCP is analyzed in detail, and an error analysis of the geomagnetic aided inertial navigation system is carried out, and then the rigid transformation is replaced with affine transformation to improve the performance of the ICCP. In contrast to the conventional approach, the proposed algorithm can solve the rotation, translation, and scaling parameters of the indicated track and the matching track, so it can significantly reduce the interference of the scale error. Experimental results confirm the effectiveness of the proposed algorithm.
Highlights
The inertial navigation system (INS) is an autonomous navigation system, which neither relies on any external information nor radiates energy to the outside world
The procedure of the iterative closest contour point (ICCP) algorithm based on affine transformation is as follows: A The inertial navigation system provides location information for N points on the Autonomous Underwater Vehicle (AUV) track; the realtime magnetic sensor provides the magnetic values of the corresponding points
We investigated the performance of the improved ICCP algorithm when there were scale errors between the indicated INS track and real track
Summary
The inertial navigation system (INS) is an autonomous navigation system, which neither relies on any external information nor radiates energy to the outside world. The traditional ICCP algorithm is easy to implement and has been widely used; it still has some deficiencies; for example, its robustness is poor and it is prone to diverging and mismatching under large initial error To resolve these problems, many methods have been put forward. Zhang [15] proposed an improved ICCP algorithm based on the particle swarm optimization algorithm, which can achieve better robustness and accuracy These researches enhanced the traditional ICCP algorithm and achieved certain results, but the algorithm can only do rigid transformation and translation transformation for the indicated track to achieve iterative registration. Xu [20] proposed an improved ICCP algorithm with affine correction in terrain matching navigation. The iterative process of matching track based on the fusion algorithm of the ICCP and affine transformation is described. The simulation experiment is conducted; the results prove the validity of the proposed algorithm
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