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Article

CK-SLAM, Crop-Row and Kinematics-Constrained SLAM for Quadruped Robots Under Corn Canopies

1
School of Information and Control Engineering, Southwest University of Science and Technology, Mianyang 621000, China
2
Mianyang Zhongke Huinong Digital Intelligence Technology Co., Ltd., Mianyang 621000, China
3
School of Economics and Management, Southwest University of Science and Technology, Mianyang 621010, China
4
School of Life Science and Engineering, Southwest University of Science and Technology, Mianyang 621010, China
5
School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang 621010, China
*
Author to whom correspondence should be addressed.
Agronomy 2026, 16(1), 95; https://doi.org/10.3390/agronomy16010095 (registering DOI)
Submission received: 20 November 2025 / Revised: 17 December 2025 / Accepted: 28 December 2025 / Published: 29 December 2025
(This article belongs to the Section Precision and Digital Agriculture)

Abstract

To address the localization and mapping challenges for quadruped robots autonomously scouting under corn canopies, this paper proposes CK-SLAM, a SLAM algorithm integrating robot motion constraints and crop row features. The algorithm is implemented on the Jueying Mini quadruped robot, fusing data from 3D LiDAR, IMU, and joint sensors. First, an Invariant Extended Kalman Filter (InEKF) fuses multi-source motion information, dynamically adjusting observation noise via a foot contact probability model (derived from joint torque data) to achieve initial motion state estimation and reliable pose references for point cloud deskewing. Second, three feature extraction schemes are designed, inheriting line/plane features from LeGO-LOAM and adding an innovative crop plane feature extraction module, which uses grid filtering, differential evolution for crop row detection, and RANSAC plane fitting to capture corn plant structural features. Finally, a two-step Levenberg–Marquardt iteration realizes feature matching and pose optimization, with factor graph optimization fusing motion constraints and laser odometry for global trajectory and map refinement. CK-SLAM effectively adapts to gait-induced measurement noise and enhances feature matching stability under canopies. Experimental validation across four corn growth stages shows it achieves an average Absolute Pose Error (APE) RMSE of 2.0939 m (15.7%/56.4%/72.2% lower than A-LOAM/LeGO-LOAM/Point-LIO) and an average Relative Pose Error (RPE) RMSE of 0.0946 m, providing high-precision navigation support for automated field monitoring.
Keywords: quadruped robot; crop row detection; InEKF; LiDAR SLAM quadruped robot; crop row detection; InEKF; LiDAR SLAM

Share and Cite

MDPI and ACS Style

Wan, M.; Luo, X.; Wu, J.; Li, L.; Tang, R.; Peng, Z.; Jiang, J.; Zhou, S.; Liu, Z. CK-SLAM, Crop-Row and Kinematics-Constrained SLAM for Quadruped Robots Under Corn Canopies. Agronomy 2026, 16, 95. https://doi.org/10.3390/agronomy16010095

AMA Style

Wan M, Luo X, Wu J, Li L, Tang R, Peng Z, Jiang J, Zhou S, Liu Z. CK-SLAM, Crop-Row and Kinematics-Constrained SLAM for Quadruped Robots Under Corn Canopies. Agronomy. 2026; 16(1):95. https://doi.org/10.3390/agronomy16010095

Chicago/Turabian Style

Wan, Mingfei, Xinzhi Luo, Jun Wu, Li Li, Rong Tang, Zhangjun Peng, Juanping Jiang, Shuai Zhou, and Zhigui Liu. 2026. "CK-SLAM, Crop-Row and Kinematics-Constrained SLAM for Quadruped Robots Under Corn Canopies" Agronomy 16, no. 1: 95. https://doi.org/10.3390/agronomy16010095

APA Style

Wan, M., Luo, X., Wu, J., Li, L., Tang, R., Peng, Z., Jiang, J., Zhou, S., & Liu, Z. (2026). CK-SLAM, Crop-Row and Kinematics-Constrained SLAM for Quadruped Robots Under Corn Canopies. Agronomy, 16(1), 95. https://doi.org/10.3390/agronomy16010095

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