Visual odometry is crucial for many robotic tasks such as autonomous exploration and path planning. Despite many progresses, existing methods are still not robust enough to dynamic illumination environments. In this paper, we present AirVO, an illumination-robust and accurate stereo visual odometry system based on point and line features. To be robust to illumination variation, we introduce the learning-based feature extraction and matching method and design a novel VO pipeline, including feature tracking, triangulation, key-frame selection, and graph optimization etc. We also employ long line features in the environment to improve the accuracy of the system. Different from the traditional line processing pipelines in visual odometry systems, we propose an illumination-robust line tracking method, where point feature tracking and distribution of point and line features are utilized to match lines. In the experiments, the proposed system is extensively evaluated in environments with dynamic illumination and the results show that it achieves superior performance to the state-of-the-art algorithms.
翻译:尽管取得了许多进展,但现有方法仍不足以适应动态照明环境。本文介绍AirVo, 一种基于点和线特征的光化-紫色和准确立体视觉测量系统。为了对光化变异产生强力作用,我们引入基于学习的特征提取和匹配方法,并设计一个新的VO管道,包括地物跟踪、三角、键框架选择和图形优化等。我们还在环境中使用长线特征来提高系统的准确性。我们建议采用不同于视觉odography系统中传统线处理管道的光化-紫色线跟踪方法,即点特征跟踪和分布点和线特征以匹配线条。在实验中,对拟议的系统进行了广泛的评价,在有动态照明的环境里,结果显示它取得了优于最先进的算法。