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

Abstract

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.

Hybrid model of alternating least squares and root polynomial technique for color correction

“…The authors developed a simple procedure called an alternating least-squares procedure to solve the color correction problem, which was originally taken as the discovery of the 333 correction matrix. The authors in [22] focused on homography estimation and the wall map as well as the camera image used for calculating homography estimation. The presented strategy was named Homography FpnNet which when applied on an image pair aligned centrally, resulted in a minute approximation error regarding other methods available.…”