Simultaneous Localization and Mapping System for Agricultural Yield Estimation Based on Improved VINS-RGBD: A Case Study of a Strawberry Field

Author:

Yuan Quanbo12ORCID,Wang Penggang2,Luo Wei234ORCID,Zhou Yongxu234,Chen Hongce2,Meng Zhaopeng1

Affiliation:

1. College of Intelligence and Computing, Tianjin University, Tianjin 300350, China

2. North China Institute of Aerospace Engineering, Langfang 065000, China

3. Aerospace Remote Sensing Information Processing and Application Collaborative Innovation Center of Hebei Province, Langfang 065000, China

4. National Joint Engineering Research Center of Space Remote Sensing Information Application Technology, Langfang 065000, China

Abstract

Crop yield estimation plays a crucial role in agricultural production planning and risk management. Utilizing simultaneous localization and mapping (SLAM) technology for the three-dimensional reconstruction of crops allows for an intuitive understanding of their growth status and facilitates yield estimation. Therefore, this paper proposes a VINS-RGBD system incorporating a semantic segmentation module to enrich the information representation of a 3D reconstruction map. Additionally, image matching using L_SuperPoint feature points is employed to achieve higher localization accuracy and obtain better map quality. Moreover, Voxblox is proposed for storing and representing the maps, which facilitates the storage of large-scale maps. Furthermore, yield estimation is conducted using conditional filtering and RANSAC spherical fitting. The results show that the proposed system achieves an average relative error of 10.87% in yield estimation. The semantic segmentation accuracy of the system reaches 73.2% mIoU, and it can save an average of 96.91% memory for point cloud map storage. Localization accuracy tests on public datasets demonstrate that, compared to Shi–Tomasi corner points, using L_SuperPoint feature points reduces the average ATE by 1.933 and the average RPE by 0.042. Through field experiments and evaluations in a strawberry field, the proposed system demonstrates reliability in yield estimation, providing guidance and support for agricultural production planning and risk management.

Funder

Central Guidance on Local Science and Technology Development Fund of Hebei Province

Publisher

MDPI AG

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