The foundation of robot autonomous movement is to quickly grasp the position and surroundings of the robot, which SLAM technology provides important support for. Due to the complex and dynamic environments, single-sensor SLAM methods often have the problem of degeneracy. In this paper, a multi-sensor fusion SLAM method based on the LVI-SAM framework was proposed. First of all, the state-of-the-art feature detection algorithm SuperPoint is used to extract the feature points from a visual-inertial system, enhancing the detection ability of feature points in complex scenarios. In addition, to improve the performance of loop-closure detection in complex scenarios, scan context is used to optimize the loop-closure detection. Ultimately, the experiment results show that the RMSE of the trajectory under the 05 sequence from the KITTI dataset and the Street07 sequence from the M2DGR dataset are reduced by 12% and 11%, respectively, compared to LVI-SAM. In simulated complex environments of animal farms, the error of this method at the starting and ending points of the trajectory is less than that of LVI-SAM, as well. All these experimental comparison results prove that the method proposed in this paper can achieve higher precision and robustness performance in localization and mapping within complex environments of animal farms.

Manual annotation of piglet imagery across varied farming environments is labor-intensive. To address this, we propose a semi-automatic approach within an active learning framework that integrates a pre-annotation model for piglet detection. We further examine how data sample composition influences pre-annotation efficiency to enhance the deployment of lactating piglet detection models. Our study utilizes original samples from pig farms in Jingjiang, Suqian, and Sheyang, along with new data from the Yinguang pig farm in Danyang. Using the YOLOv5 framework, we constructed both single and mixed training sets of piglet images, evaluated their performance, and selected the optimal pre-annotation model. This model generated bounding box coordinates on processed new samples, which were subsequently manually refined to train the final model. Results indicate that expanding the dataset and diversifying pigpen scenes significantly improve pre-annotation performance. The best model achieved a test precision of 0.921 on new samples, and after manual calibration, the final model exhibited a training precision of 0.968, a recall of 0.952, and an average precision of 0.979 at the IoU threshold of 0.5. The model demonstrated robust detection under various lighting conditions, with bounding boxes closely conforming to piglet contours, thereby substantially reducing manual labor. This approach is cost-effective for piglet segmentation tasks and offers strong support for advancing smart agricultural technologies.

Body weight and size are critical indicators of sow health and reproductive performance. Traditional manual measurement methods are not only time-consuming and labor-intensive but also induce stress in sows. To address these limitations, we propose an innovative method for estimating sow body weight and size using 3D point cloud data. Our method began by obtaining point cloud data from depth images captured by an Intel^® RealSense™ D455 camera. First, we used a KPConv segmentation model with a deformable kernel to extract the sow‘s back. The resulting back point cloud then served as the input to a novel dual-branch, multi-output regression model named DbmoNet, which integrates features from both location and feature spaces. We evaluated the method on 2400 samples from three breeds during non-pregnant periods. The KPConv model demonstrated excellent performance, achieving an overall segmentation accuracy (OA) of 99.54%. The proposed DbmoNet model outperformed existing benchmarks, achieving mean absolute percentage errors (MAPEs) of 3.74% for body weight (BW), 3.97% for chest width (CW), 3.33% for hip width (HW), 3.82% for body length (BL), 1.94% for chest height (CH), and 2.43% for hip height (HH). Therefore, this method provides an accurate and efficient tool for non-contact body condition monitoring in intensive sow production.