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Open AccessArticle

Deep Homography Estimation and Its Application to Wall Maps of Wall-Climbing Robots

by and *
The State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China
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Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(14), 2908; https://doi.org/10.3390/app9142908
Received: 21 June 2019 / Revised: 13 July 2019 / Accepted: 17 July 2019 / Published: 20 July 2019
(This article belongs to the Special Issue Intelligent Robotics)
When locating wall-climbing robots with vision-based methods, locating and controlling the wall-climbing robot in the pixel coordinate of the wall map is an effective alternative that eliminates the need to calibrate the internal and external parameters of the camera. The estimation accuracy of the homography matrix between the camera image and the wall map directly impacts the pixel positioning accuracy of the wall-climbing robot in the wall map. In this study, we focused on the homography estimation between the camera image and wall map. We proposed HomographyFpnNet and obtained a smaller homography estimation error for a center-aligned image pair compared with the state of the art. The proposed hierarchical HomographyFpnNet for a non-center-aligned image pair significantly outperforms the method based on artificially designed features + Random Sample Consensus. The experiments conducted with a trained three-stage hierarchical HomographyFpnNet model on wall images of climbing robots also achieved small mean corner pixel error and proved its potential for estimating the homography between the wall map and camera images. The three-stage hierarchical HomographyFpnNet model has an average processing time of 10.8 ms on a GPU. The real-time processing speed satisfies the requirements of wall-climbing robots. View Full-Text
Keywords: homography estimation; convolutional neural network; wall-climbing robot homography estimation; convolutional neural network; wall-climbing robot
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MDPI and ACS Style

Zhou, Q.; Li, X. Deep Homography Estimation and Its Application to Wall Maps of Wall-Climbing Robots. Appl. Sci. 2019, 9, 2908.

AMA Style

Zhou Q, Li X. Deep Homography Estimation and Its Application to Wall Maps of Wall-Climbing Robots. Applied Sciences. 2019; 9(14):2908.

Chicago/Turabian Style

Zhou, Qiang; Li, Xin. 2019. "Deep Homography Estimation and Its Application to Wall Maps of Wall-Climbing Robots" Appl. Sci. 9, no. 14: 2908.

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