In a GPS-hindered indoor environment, precise positioning is a challenge for mobile robots that perform accurate operations. For instance, during ridge-raising operations in plant factories, robots require positioning accuracy upto 5 cm, which is a pending issue in industry. To address this problem, we propose a ultraprecise simultaneous localization and mapping (SLAM) positioning method, namely, preciseSLAM, which tightly couples LiDAR, inertial measurement unit (IMU), and ultrasonic sensors to achieve ultraprecision, robustness, and real-time positioning performance. First, the preciseSLAM framework is established by fusing multimodal data from LiDAR, IMU, and ultrasonic sensors. Second, to effectively reduce naive SLAM positioning drift after long-term operation, a confidence zone optimization method is proposed for ultrasonic global positioning information. Finally, a factor graph optimization algorithm is developed to tightly couple four preciseSLAM factors, i.e., LiDAR odometry, IMU odometry, loop closure, and the specific ultrasonic global positioning factor. The proposed method effectively addresses the challenges in plant factory environments and can provide reliable and accurate positioning for intelligent robots. Experimental results demonstrate that preciseSLAM achieves a remarkable positioning accuracy of 5.4 cm in the indoor environment inside a plant factory. Compared with that of existing methods, preciseSLAM exhibits ultraprecision, superior robustness, and real-time performance. preciseSLAM provides a general-purpose positioning and navigation solution for robots in large-scale plant factories, which is among the very few methods that can deal with precise indoor positioning.

Orchard robots tasks rely on highly accurate, real-time trajectory estimation and map-building. In this paper, a novel tightly-coupled LIDAR inertial simultaneous localization and mapping (SLAM) system, SG-ISBP-SLAM, is proposed. The algorithm involves both ground optimization and loop closure detection. Aiming at the uneven orchard ground, this paper designs a ground segmentation method from a global perspective. It takes the nearest neighbor seed plane as the base-line and iteratively grows a global plane based on principal component analysis (PCA). The LIDAR scan is divided into 3D concentric zone representations to assign an appropriate density of cloud points among bins. Based on the partition strategy, the improved spatial binary pattern (ISBP) is encoded for lower time-consuming loop closure detection. To validate the performance of the proposed algorithm, qualitative and quantitative experiments have been conducted. Experimental results indicate that SG-ISBP-SLAM provides low-time consumption and reliable non-flat ground segmentation capabilities. Moreover, the loop module can efficiently correct robot localization trajectory drift.

In the process of collaborative operation, the unloading automation of the forage harvester is of great significance to improve harvesting efficiency and reduce labor intensity. However, non-standard transport trucks and unstructured field environments make it extremely difficult to identify and properly position loading containers. In this paper, a global model with three coordinate systems is established to describe a collaborative harvesting system. Then, a method based on depth perception is proposed to dynamically identify and position the truck container, including data preprocessing, point cloud pose transformation based on the singular value decomposition (SVD) algorithm, segmentation and projection of the upper edge, edge lines extraction and corner points positioning based on the Random Sample Consensus (RANSAC) algorithm, and fusion and visualization of results on the depth image. Finally, the effectiveness of the proposed method has been verified by field experiments with different trucks. The results demonstrated that the identification accuracy of the container region is about 90%, and the absolute error of center point positioning is less than 100 mm. The proposed method is robust to containers with different appearances and provided a methodological reference for dynamic identification and positioning of containers in forage harvesting.

Semi-structured greenhouse environment often features repetitive and weak texture, naturally bringing challenges to high-precision vision-based positioning techniques. This paper proposes a precise visual positioning method for agricultural mobile robots in the greenhouse, which improves their positioning accuracy via discriminatively minimizing fiducial marker reprojection errors. First, fiducial markers are used to enhance environment features, and a markers-based visual positioning task is formulated as a Perspective-n-Point (PnP) problem. The projection constraints of keypoints and the pose constraints of the coordinate systems provide a theoretical basis for robot positioning. Second, a reprojection error minimization approach is proposed by taking into account the markers’ distance and image noise. Far-away markers are more prone to greater observation errors than those close to the robots, the improved PnP algorithm considering distance weighting ensures higher positioning accuracy. Synthetic and field experiments are carried out to evaluate the performance of the proposed method. Synthetic experiments show that the rotation error and translation error of the proposed method are less than 0.7° and 0.5% within a range of 12 m. The mean absolute error and root mean square error of field dynamic positioning experiments are 8.57 cm and 8.59 cm, respectively. Experimental results show that the proposed method is significantly better than traditional methods in dealing with distance-related noise at keypoints.