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arXiv · 2203.13861

FD-SLAM: 3-D Reconstruction Using Features and Dense Matching

Xingrui Yang·Yuhang Ming·Zhaopeng Cui·Andrew Calway
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Abstract

It is well known that visual SLAM systems based on dense matching are locally accurate but are also susceptible to long-term drift and map corruption. In contrast, feature matching methods can achieve greater long-term consistency but can suffer from inaccurate local pose estimation when feature information is sparse. Based on these observations, we propose an RGB-D SLAM system that leverages the advantages of both approaches: using dense frame-to-model odometry to build accurate sub-maps and on-the-fly feature-based matching across sub-maps for global map optimisation. In addition, we incorporate a learning-based loop closure component based on 3-D features which further stabilises map building. We have evaluated the approach on indoor sequences from public datasets, and the results show that it performs on par or better than state-of-the-art systems in terms of map reconstruction quality and pose estimation. The approach can also scale to large scenes where other systems often fail.

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Xingrui Yang, Yuhang Ming, Zhaopeng Cui, Andrew Calway. 2022-03-25. FD-SLAM: 3-D Reconstruction Using Features and Dense Matching. https://doi.org/10.1109/icra46639.2022.9812049

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