不依赖特征点的快速位姿估计，适合低纹理环境实时应用。
A Fast and Light-weight Non-Iterative Visual Odometry with RGB-D Cameras
- 分离旋转与平移估计，用平面重叠特征和核互相关计算
- 在低配i5 CPU上达71Hz，避免迭代优化耗时
- 无特征点设计，对低纹理场景更鲁棒，适合嵌入式部署
本文提出一种高效估计6自由度机器人位姿的新方法，采用解耦的非迭代策略，利用场景中的重叠平面元素。传统RGB-D视觉里程计常依赖迭代优化求解位姿，并涉及特征提取与匹配过程，带来显著计算负担与延迟。本方法将旋转与平移估计分离：首先利用场景中重叠的平面特性计算旋转矩阵；随后采用核互相关（KCC）确定平移量。通过跳过资源密集型的迭代优化及特征提取对齐流程，该方法在低端i5 CPU上实现71Hz的运行速度。当不依赖特征点时，在低纹理退化环境下性能优于现有先进方法。
原文摘要 · Abstract (English)
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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