AI Article Synopsis
- The study focuses on improving SLAM algorithms for agricultural robots in greenhouses, particularly addressing challenges posed by low-density canopies affecting robustness and accuracy.
- An innovative AF-PCP SLAM algorithm was developed, utilizing multiline LiDAR and adaptive filtering to enhance mapping and localization of crops, showing a substantial improvement over existing methods like the Cartographer algorithm.
- Experimental results indicate that the AF-PCP SLAM algorithm significantly increases mapping area, reduces localization errors, and demonstrates reliable performance across various speeds in low-density greenhouse environments.
Article Abstract
To address the problem that the low-density canopy of greenhouse crops affects the robustness and accuracy of simultaneous localization and mapping (SLAM) algorithms, a greenhouse map construction method for agricultural robots based on multiline LiDAR was investigated. Based on the Cartographer framework, this paper proposes a map construction and localization method based on spatial downsampling. Taking suspended tomato plants planted in greenhouses as the research object, an adaptive filtering point cloud projection (AF-PCP) SLAM algorithm was designed. Using a wheel odometer, 16-line LiDAR point cloud data based on adaptive vertical projections were linearly interpolated to construct a map and perform high-precision pose estimation in a greenhouse with a low-density canopy environment. Experiments were carried out in canopy environments with leaf area densities (LADs) of 2.945-5.301 m/m. The results showed that the AF-PCP SLAM algorithm increased the average mapping area of the crop rows by 155.7% compared with that of the Cartographer algorithm. The mean error and coefficient of variation of the crop row length were 0.019 m and 0.217%, respectively, which were 77.9% and 87.5% lower than those of the Cartographer algorithm. The average maximum void length was 0.124 m, which was 72.8% lower than that of the Cartographer algorithm. The localization experiments were carried out at speeds of 0.2 m/s, 0.4 m/s, and 0.6 m/s. The average relative localization errors at these speeds were respectively 0.026 m, 0.029 m, and 0.046 m, and the standard deviation was less than 0.06 m. Compared with that of the track deduction algorithm, the average localization error was reduced by 79.9% with the proposed algorithm. The results show that our proposed framework can map and localize robots with precision even in low-density canopy environments in greenhouses, demonstrating the satisfactory capability of the proposed approach and highlighting its promising applications in the autonomous navigation of agricultural robots.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10867628 | PMC |
| http://dx.doi.org/10.3389/fpls.2024.1276799 | DOI Listing |
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Article Synopsis
- The study focuses on improving SLAM algorithms for agricultural robots in greenhouses, particularly addressing challenges posed by low-density canopies affecting robustness and accuracy.
- An innovative AF-PCP SLAM algorithm was developed, utilizing multiline LiDAR and adaptive filtering to enhance mapping and localization of crops, showing a substantial improvement over existing methods like the Cartographer algorithm.
- Experimental results indicate that the AF-PCP SLAM algorithm significantly increases mapping area, reduces localization errors, and demonstrates reliable performance across various speeds in low-density greenhouse environments.
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