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In response to the poor adaptability of a single sensor in facing complex field environments, a maize crop row detection method was proposed based on the fusion of solid-state LiDAR and RGB camera. Firstly, a joint calibration method for solid-state LiDAR and RGB camera was studied to simultaneously acquire maize crop row images and point cloud data for data preprocessing. Next, the preprocessed image data and point cloud data were fused to achieve point cloud “coloring”, and a clustering algorithm based on point cloud “coloring” for detecting regions of interest was proposed. The clustering was done by using the “colored” point cloud, and the availability of both point cloud and color information was separately validated based on crop planting agronomic standards (row spacing) to cluster the regions of interest effectively. Finally, by dividing the point cloud into horizontal strips, the feature points of the target point cloud were clustered to identify crop row feature points, and a crop row detection line was fitted by using the least squares method. By adjusting only the row spacing parameter, the algorithm can achieve crop row detection throughout the crop lifecycle. The algorithm’s performance was verified by using data from maize seedling, early, mid, and late stages under normal conditions, with average centerline error not more than 1.781°, accuracy not less than 92.69%, and average processing time not more than 102.7 ms. Furthermore, to test the algorithm’s robustness, crop row detections under four challenging conditions, including high weed background, missing rows, weed height similar to maize height, and completely closed rows, were conducted in complex agricultural field backgrounds. The algorithm showed an average error of not more than 1.935°, accuracy not less than 91.94%, and an average processing time not more than 108.3 ms. Discussions highlighted the superiority of using point cloud “coloring” for extracting crop row centerline, providing a reliable approach for detecting crop row centerlines.