Open AccessDevelopment of marker detection method for estimating angle and distance of underwater remotely operated vehicle to buoyant boat
Author(s) -
Muhammad Qomaruz Zaman,
Ronny Mardiyanto
Publication year - 2021
Publication title -
ijain (international journal of advances in intelligent informatics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.183
H-Index - 9
eISSN - 2548-3161
pISSN - 2442-6571
DOI - 10.26555/ijain.v7i3.455
Subject(s) - remotely operated underwater vehicle , payload (computing) , artificial intelligence , computer science , computer vision , underwater , orientation (vector space) , word error rate , remote sensing , mathematics , robot , geology , mobile robot , computer network , oceanography , geometry , network packet
The paper proposes a Marker Detection Method for Estimating the Angle and Distance of Underwater Remotely Operated Vehicle (ROV) to Buoyant Boat. To keep the ROV aligned with the boat, a marker and visual recognition system are designed. The marker is placed facing down under the boat and a method is developed to recognize the angle and distance of the marker from a facing up camera on the ROV. By considering space, payload, heat dissipation, and buoyancy in a micro class ROV, there are limited options for computing power that can be utilized. This challenge demands a lightweight visual recognition technique for small computers. The proposed method consists of two steps. The marker designing step explains how the marker is constructed of simple components. The marker recognizing step is based on image processing that uses threshold and blob filtering. They are blob size and blob circularity filters which are used to eliminate unwanted information. The real-time orientation and distance estimation by using one camera are the superiority of this method. The proposed method has been tested by using an 11x11 cm2 marker size. The detection rate of the marker is 90% and can be detected up to 120 cm from the camera. The marker can be tilted up to 50° and still has an 80% detection rate. The method can estimate marker rotation angle accurately with a 1.75° average error. The method can estimate the distance between the marker and camera with a -0.62 cm average error. The blob filter is also proven to be superior to a regular dilating and eroding method.