The authors improve monocular SLAM scale estimation with a deeper CNN plus bidirectional LSTM, and show synthetic-only training reaches accuracies comparable to real-data training.
Ground Plane based Absolute Scale Estimation for Monocular Visual Odometry
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abstract
Recovering the absolute metric scale from a monocular camera is a challenging but highly desirable problem for monocular camera-based systems. By using different kinds of cues, various approaches have been proposed for scale estimation, such as camera height, object size etc. In this paper, firstly, we summarize different kinds of scale estimation approaches. Then, we propose a robust divide and conquer the absolute scale estimation method based on the ground plane and camera height by analyzing the advantages and disadvantages of different approaches. By using the estimated scale, an effective scale correction strategy has been proposed to reduce the scale drift during the Monocular Visual Odometry (VO) estimation process. Finally, the effectiveness and robustness of the proposed method have been verified on both public and self-collected image sequences.
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Estimation of Absolute Scale in Monocular SLAM Using Synthetic Data
The authors improve monocular SLAM scale estimation with a deeper CNN plus bidirectional LSTM, and show synthetic-only training reaches accuracies comparable to real-data training.