Monocular Visual-Inertial Odometry Based on Sparse Feature Selection with Adaptive Grid

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Monocular Visual-Inertial Odometry Based on Sparse Feature Selection with Adaptive Grid

Cai, Zhiao / Yang, Ming / Wang, Chunxiang / Wang, Bing

For sparse feature based visual-inertial odometry, feature selection is vital to the performance. The selected features should be evenly distributed in the image and be appropriate for tracking. This paper presents a visual-inertial odometry approach based on sparse feature selection with the adaptive grid to improve the performance in different environments. In the proposed approach, FAST corner detection is employed in every grid, the size of which is adaptively adjusted. Features in the same grid are ranked and selected based on scores to ensure good feature quality. Subsequently, the selected features are tracked by the KLT sparse optical flow with local intensity normalization. Finally, the inertial and visual measurements are jointly optimized by sliding window based nonlinear optimization to achieve six degrees of freedom motion estimation. The proposed method is validated on the public dataset and real-world experiments, which shows our approach reaches state-of-the-art performance.