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

TL;DR

This paper presents a fast, non-iterative RGB-D visual odometry method that decouples rotation and translation estimation, significantly reducing computational load and improving performance in low-texture environments.

Contribution

It introduces a novel non-iterative approach for RGB-D visual odometry that leverages planar scene features and kernel cross-correlation, avoiding traditional iterative optimization.

Findings

Achieves 71Hz processing speed on a low-end CPU.

Outperforms state-of-the-art methods in low-texture environments.

Reduces computational complexity by decoupling rotation and translation estimation.

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.