Point-line feature fusion based field real-time RGB-D SLAM

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3D reconstruction of crops is important for researching their biological properties, canopy light distribution and robotic harvesting. However, the complex field environment makes the real-time 3D reconstruction of crops difficult. Due to the low-textured in the field, it is difficult to obtain effective features to construct accurate and real-time 3D maps of the field from existing single-feature SLAM methods. In this paper, we propose a novel RGB-D SLAM based on point-line feature fusion for the real-time field 3D scene reconstruction. By optimizing the point-line features joint poses, we first build a 3D scene map of the field based on the point-line feature structure. Then, a joint point cloud filtering method is designed based on the keyframes optimization of the point-line feature. Finally, we obtain the consistently high-quality dense map in the global respect. The overall performance in terms of pose estimation and reconstruction is evaluated on public benchmarks and shows improved performance compared to state-of-the-art methods. Qualitative experiments on the field scenes show that our method enables real-time 3D reconstruction of crops with high robustness.