Camera Orientation Estimation in Leaking Indoor Environment via Vanishing Point of Water Drops

In this paper, we propose a novel camera orientation estimation method based on the computation of the vanishing point of water drops in leaking indoor environment. Camera orientation estimation is an important component of robots as it allows them to perform complex tasks such as three-dimensional (3D) reconstruction of different environments. Camera estimation usually involves sensors, such as cameras or encoders and sophisticated processing algorithms. In recent years, computer vision techniques have been widely used to estimate the camera orientation in robotics-related research as visual sensing can improve the autonomy of the systems. Although most of these methods perform well in outdoor environments, they are problematic in the environments of indoor disasters, where common visual features may be missing due to collapse and erosion. To solve these problems, we developed a novel technique that employs particular characteristics of leaking indoor environment. Our method uses the vanishing point generated form the trajectories of water drops, to estimate the rotation of the camera. The proposed technique can potentially be applied for inspecting nuclear power plants. Computer-simulated and real data experiments have been performed to evaluate the accuracy of the proposed method. The results of these experiments demonstrate that our method can detect the vanishing point of water drops and estimate the rotation angle accurately.

[1]  D. Hamonic,et al.  Radiation hardening methodology applied on absolute optical encoder of a total dose beyond 100 kGy , 1999, 1999 Fifth European Conference on Radiation and Its Effects on Components and Systems. RADECS 99 (Cat. No.99TH8471).

[2]  Andreu Corominas Murtra,et al.  IMU and cable encoder data fusion for in-pipe mobile robot localization , 2013, 2013 IEEE Conference on Technologies for Practical Robot Applications (TePRA).

[3]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Juan D. Tardós,et al.  ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras , 2016, IEEE Transactions on Robotics.

[5]  Ferdinand van der Heijden,et al.  Efficient adaptive density estimation per image pixel for the task of background subtraction , 2006, Pattern Recognit. Lett..

[6]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[7]  Giuseppe Loianno,et al.  Autonomous Inspection of a Containment Vessel using a Micro Aerial Vehicle , 2019, 2019 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR).

[8]  Reinhard Koch,et al.  Pose Estimation from Line Correspondences: A Complete Analysis and a Series of Solutions , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Kyle O'Keefe,et al.  Monocular-based pose estimation using vanishing points for indoor image correction , 2017, 2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[10]  N. Otsu A threshold selection method from gray level histograms , 1979 .

[11]  J. J. Moré,et al.  Levenberg--Marquardt algorithm: implementation and theory , 1977 .

[12]  Atsushi Yamashita,et al.  3D radiation imaging using mobile robot equipped with radiation detector , 2017, 2017 IEEE/SICE International Symposium on System Integration (SII).

[13]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .