Visual odometry,which aims to estimate relative camera motion between sequential video frames,has been widely used in the fields of augmented reality,virtual reality,and autonomous driving.However,it is still quite challenging for state-of-the-art approaches to handle low-texture scenes.In this paper,we propose a robust and efficient visual odometry algorithm that directly utilizes edge pixels to track camera pose.In contrast to direct methods,we choose reprojection error to construct the optimization energy,which can effectively cope with illumination changes.The distance transform map built upon edge detection for each frame is used to improve tracking efficiency.A novel weighted edge alignment method together with sliding window optimization is proposed to further improve the accuracy.Experiments on public datasets show that the method is comparable to state-of-the-art methods in terms of tracking accuracy,while being faster and more robust.

Feihu Yan;Zhaoxin Li;Zhong Zhou

State Key Laboratory of Virtual Reality Technology and Systems,Beihang University,Beijing 100191,China;Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China

计算可视媒体（英文）

[1]Feihu Yan;Zhaoxin Li;Zhong Zhou-.Robust and efficient edge-based visual odometry)[J].计算可视媒体（英文）,2022(03):467-481

reprojection

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