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Error Improvement in Visual Odometry Using Super-resolution

Wonyeong Jeong*, Jiyoun Moon, and Beomhee Lee
International Journal of Control, Automation, and Systems, vol. 18, no. 2, pp.322-329, 2020

Abstract : Visual odometry (VO), a method that estimates odometry using visual sensors, is hard to operate successfully with the low-resolution and noisy image sequences. To address this problem, a super-resolution technique is applied to input data before performing VO. Since most conventional super-resolution literature mainly deals with the resolution increment, we present a novel deep neural super-resolution network, which can remove noises as well. The execution time is also taken into account by adjusting the number of CNN layers for a real-time VO. By applying the proposed super-resolution approach, the resolution increases and noises disappear with a suitable speed, hence VO can be performed successfully. Experimental results show that the proposed method improves the VO performance compared with the conventional VO which uses low-resolution and noisy image sequences.

Keyword : Robust Visual Odometry, super-resolution, visual odometry, visual SLAM.

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