Visual odometry is important for plenty of applications such as autonomous vehicles, and robot navigation. It is challenging to conduct visual odometry in textureless scenes or environments with sudden illumination changes where popular feature-based methods or direct methods cannot work well. To address this challenge, some edge-based methods have been proposed, but they usually struggle between the efficiency and accuracy. In this work, we propose a novel visual odometry approach called \textit{EdgeVO}, which is accurate, efficient, and robust. By efficiently selecting a small set of edges with certain strategies, we significantly improve the computational efficiency without sacrificing the accuracy. Compared to existing edge-based method, our method can significantly reduce the computational complexity while maintaining similar accuracy or even achieving better accuracy. This is attributed to that our method removes useless or noisy edges. Experimental results on the TUM datasets indicate that EdgeVO significantly outperforms other methods in terms of efficiency, accuracy and robustness.

翻译：视觉里程计在自动驾驶车辆和机器人导航等众多应用中具有重要意义。在纹理缺失场景或光照突变环境下，流行的基于特征的方法或直接方法难以有效工作，因此进行视觉里程计具有挑战性。为解决这一挑战，已提出部分基于边缘的方法，但这些方法通常难以兼顾效率与精度。本研究提出一种名为EdgeVO的新型视觉里程计方法，该方法兼具准确性、高效性和鲁棒性。通过采用特定策略高效选取少量边缘，我们在不牺牲精度的前提下显著提升了计算效率。与现有基于边缘的方法相比，本方法在保持相近精度甚至实现更高精度的同时，可大幅降低计算复杂度。这一优势归功于本方法有效去除了无用或噪声边缘。在TUM数据集上的实验结果表明，EdgeVO在效率、精度和鲁棒性方面均显著优于其他方法。