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arXiv · 2507.18886

A Fast and Light-weight Non-Iterative Visual Odometry with RGB-D Cameras

Zheng Yang·Kuan Xu·Shenghai Yuan·Lihua Xie
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Abstract

In this paper, we introduce a novel approach for efficiently estimating the 6-Degree-of-Freedom (DoF) robot pose with a decoupled, non-iterative method that capitalizes on overlapping planar elements. Conventional RGB-D visual odometry(RGBD-VO) often relies on iterative optimization solvers to estimate pose and involves a process of feature extraction and matching. This results in significant computational burden and time delays. To address this, our innovative method for RGBD-VO separates the estimation of rotation and translation. Initially, we exploit the overlaid planar characteristics within the scene to calculate the rotation matrix. Following this, we utilize a kernel cross-correlator (KCC) to ascertain the translation. By sidestepping the resource-intensive iterative optimization and feature extraction and alignment procedures, our methodology offers improved computational efficacy, achieving a performance of 71Hz on a lower-end i5 CPU. When the RGBD-VO does not rely on feature points, our technique exhibits enhanced performance in low-texture degenerative environments compared to state-of-the-art methods.

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Zheng Yang, Kuan Xu, Shenghai Yuan, Lihua Xie. 2025-07-25. A Fast and Light-weight Non-Iterative Visual Odometry with RGB-D Cameras. https://doi.org/10.1142/s2301385025500591

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