To realize the accurate 3D shape real-time estimation of soft manipulator and lay the foundation for deformation control and application

a real-time 3D shape recognition method based on the SOM algorithm is proposed for three-section soft manipulator. Firstly

the left and right frame data captured by ZED binocular camera are preprocessed to obtain the left and right binary images

with the 2D contour data of the soft manipulator extracted in real time. Then

the self-organizing map (SOM) algorithm is run to cluster the contour data to obtain the 2D centerline of the soft manipulator in order

and compared with K-means

Gaussian mixture model and thinning of three centerline extraction algorithms

revealing that SOM algorithm is more suitable for solving the centerline identification of complex shape of the soft manipulator. Finally

the 3D shape reconstruction of the soft manipulator is completed by using the triangulation model based on the disparity. Furthermore

the algorithm framework adapts data downsampling

SOM parameter optimization and other methods to improve real-time performance. The real-time shape sensing and comparative verification are carried out during the continuous deformation of the soft manipulator. Experimental results show that the algorithm presents a high shape sensing accuracy and a superior real-time tracking effect. Compared with other shape detection algorithms locally and globally

the proposed algorithm displays a better performance.