Abstract
With the extensive application of robots, such as unmanned aerial vehicle (UAV) in exploring unknown environments, visual odometry (VO) algorithms have played an increasingly important role. The environments are diverse, not always textured, or low-textured with insufficient features, making them challenging for mainstream VO. However, for low-texture environment, due to the structural characteristics of man-made scene, the lines are usually abundant. In this paper, we propose a virtual-real hybrid map based monocular visual odometry algorithm. The core idea is that we reprocess line segment features to generate the virtual intersection matching points, which can be used to build the virtual map. Introducing virtual map can improve the stability of the visual odometry algorithm in low-texture environment. Specifically, we first combine unparallel matched line segments to generate virtual intersection matching points, then, based on the virtual intersection matching points, we triangulate to get a virtual map, combined with the real map built upon the ordinary point features to form a virtual-real hybrid 3D map. Finally, using the hybrid map, the continuous camera pose estimation can be solved. Extensive experimental results have demonstrated the robustness and effectiveness of the proposed method in various low-texture scenes.
Highlights
In recent years, intelligent robots have been widely developed and deployed
By exploring the properties of the virtual intersection matching points, we propose a virtual-real hybrid map based monocular visual odometry (VO) algorithm combined with points and line segments
We defined a concept of LSVI matching points and demonstrated that the virtual map constructed from the LSVI matching points can be used to effectively estimate the camera pose
Summary
Intelligent robots have been widely developed and deployed. For instance, as a kind of robot, unmanned aerial vehicle (UAV) has been widely used for resource surveys [1,2], terrain modeling [3], disaster rescue [4] and other applications.For robots, visual odometry (VO) and visual Simultaneous Localization and Mapping (vSLAM) are the key technologies. Intelligent robots have been widely developed and deployed. A robot can accurately estimate its trajectory in an unknown environment, which endows it with the ability to explore unknown environments on its own [5,6,7,8]. Possible environments are highly diverse, not always textured, and there are plenty of low-texture environments, such as the streets in urban areas and artificial squares. In these environments, there are no rich textures, but lines are much more abundant due to the structure of the man-made scene
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