Design and experiments with a SLAM system for low-density canopy environments in greenhouses based on an improved Cartographer framework

Haoran Tan; Xueguan Zhao; Changyuan Zhai; Hao Fu; Liping Chen; Minli Yang

doi:10.3389/fpls.2024.1276799

Design and experiments with a SLAM system for low-density canopy environments in greenhouses based on an improved Cartographer framework

Front Plant Sci. 2024 Jan 29:15:1276799. doi: 10.3389/fpls.2024.1276799. eCollection 2024.

Authors

Haoran Tan^#^{1

2}, Xueguan Zhao^#^{2

3

4}, Changyuan Zhai^{2

3}, Hao Fu², Liping Chen^{2

3}, Minli Yang^#¹

Affiliations

¹ College of Engineering, China Agricultural University, Beijing, China.
² Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
³ National Engineering Research Center for Information Technology in Agriculture, Beijing, China.
⁴ Beijing PAIDE Science and Technology Development Co., Ltd, Beijing, China.

^# Contributed equally.

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 m²/m³. 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.

Keywords: greenhouse; lidar; mobile robot; perception; simultaneous localization and mapping (SLAM).

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was financially supported by the Jiangsu Province Agricultural Science and Technology Independent Innovation Fund Project (Grant number: CX ( 21 ) 2006 ), the Jiangsu Province Key Research and Development Plan Project (BE2021302), the Youth Research Fund of the Beijing Academy of Agriculture and Forestry Sciences (Grant number: QNJJ202013), the Beijing Academy of Agricultural and Forestry Sciences Science and Technology Innovation Capacity Construction Project (KJCX20210402).