ArticleFrontiers in plant science2024
Design and experiments with a SLAM system for low-density canopy environments in greenhouses based on an improved Cartographer framework.
Article in Frontiers in plant science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 14 citations in OpenAlex.
- PhenoRob-P: An autonomous robotic system for high-throughput phenotyping of potted plants.Plant phenomics (Washington, D.C.) · 2026Article
- Deep learning-based 3D morphological segmentation and quantitative growth analysis of field-grown cabbage across the full cycle.Plant phenomics (Washington, D.C.) · 2026Article
- Optimization of a multi-environmental detection model for tomato growth point buds based on multi-strategy improved YOLOv8.Scientific reports · 2025Article
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Authors and funding
6 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
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
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