Non-iterative object detection methods in electrical tomography for robotic applications

Abstract

Purpose

This paper aims to investigate the usability of the non-iterative monotonicity approach for electrical capacitance tomography (ECT)-based object detection. This is of particular importance with respect to object detection in robotic applications.

Design/methodology/approach

With respect to the detection problem, the authors propose a precomputed threshold value for the exclusion test to speed up the algorithm. Furthermore, they show that the use of an inhomogeneous split-up strategy of the region of interest (ROI) improves the performance of the object detection.

Findings

The proposed split-up strategy enables to use the monotonicity approach for robotic applications, where the spatial placement of the electrodes is constrained to a planar geometry. Additionally, owing to the improvements in the exclusion tests, the selection of subregions in the ROI allows for avoiding self-detection. Furthermore, the computational costs of the algorithm are reduced owing to the use of a predefined threshold, while the detection capabilities are not significantly influenced.

Originality/value

The presented simulation results show that the adapted split-up strategies for the ROI improve significantly the detection performance in comparison to the traditional ROI split-up strategy. Thus, the monotonicity approach becomes applicable for ECT-based object detection for applications, where only a reduced number of electrodes with constrained spatial placement can be used, such as in robotics.

Citation

Mühlbacher-Karrer, S., Padilha Leitzke, J., Faller, L.-M. and Zangl, H. (2017), "Non-iterative object detection methods in electrical tomography for robotic applications", COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, Vol. 36 No. 5, pp. 1411-1420. https://doi.org/10.1108/COMPEL-02-2017-0092