In the study of animal behavior, the prevention of sickness, and the gait planning of legged robots, pose estimation, and gait parameter extraction of quadrupeds are of tremendous importance. However, there are several varieties of quadrupeds, and distinct species frequently have radically diverse body types, limb configurations, and gaits. Currently, it is challenging to forecast animal pose estimation with any degree of accuracy. This research developed a quadruped animal pose estimation and gait parameter extraction method to address this problem. A computational framework including three components of target screening, animal pose estimation model, and animal gaits parameter extraction, which can totally and efficiently solve the problem of quadruped animal pose estimation and gait parameter extraction, makes up its core. On the basis of the HRNet network, an improved quadruped animal keypoint extraction network, RFB-HRNet, was proposed to enhance the extraction effect of quadruped pose estimation. The basic concept was to use a DyConv (dynamic convolution) module and an RFB (receptive field block) module to propose a special receptive field module DyC-RFB to optimize the feature extraction capability of the HRNet network at stage 1 and to enhance the feature extraction capability of the entire network model. The public dataset AP10K was then used to validate the model’s performance, and it was discovered that the proposed method was superior to alternative methods. Second, a two-stage cascade network was created by adding an object detection network to the front end of the pose estimation network to filter the animal object in input images, which enhanced the pose estimation effect of small targets and multitargets. The acquired keypoints data of animals were then utilized to extract the gait parameters of the experimental objects. Experiment findings showed that the gait parameter extraction model proposed in this research could effectively extract the gait frequency, gait sequence, gait duty cycle, and gait trajectory parameters of quadruped animals, and obtain real-time and accurate gait trajectory.
SLAM (simultaneous localization and mapping) technology has recently shown considerable forward progress; however, most of the mainstream SLAM technologies are currently based on laser- and vision-based fusion strategies. However, there are problems (e.g., a lack of geometric structure, no significant feature points in the surrounding environment, LiDAR degradation, and the longitudinal loss of constraints, as well as missing GPS signals within the pipeline) in special circumstances (e.g., in underground pipelines and tunnels), thus making it difficult to apply laser or vision SLAM techniques. To solve this issue, a multi-robot cooperation-based SLAM method is proposed in this study for pipeline environments, based on LIO-SAM. The proposed method can effectively perform SLAM tasks in spaces with high environmental similarity (e.g., tunnels), thus overcoming the limitation that existing SLAM methods have been poorly applied in pipeline environments due to the high environmental similarity. In this study, the laser-matching part of the LIO-SAM is removed, and a high-precision differential odometer, IMU inertial navigation sensor, and an ultrasonic sensor, which are not easily affected by the high similarity of the environment, are employed as the major sources of positioning information. Moreover, a front-to-back queue of two robots is trained in the pipeline environment; a unique period-creep method has been designed as a cooperation strategy between the two robots, and a multi-robot pose constraint factor (ultrasonic range factor) is built to constrain the robots’ relative poses. On that basis, the robot queue can provide a mutual reference when traveling through the pipeline and fulfill its pose correction with high quality, thus achieving high positioning accuracy. To validate the method presented in this study, four experiments were designed, and SLAM testing was performed in common environments, as well as simple and complex urban pipeline environments. Next, error analysis was conducted using EVO. The experimental results suggest that the method proposed in this study is less susceptible to environmental effects than the existing methods due to the benefits of multi-robot cooperation. This applies to a common environment (e.g., a room) and can achieve a good performance; this means that a wide variety of piping environments can be established with high similarity. The average error of SLAM in the pipeline was 0.047 m, and the maximum error was 0.713 m, such that the proposed method shows the advantages of controllable cumulative error, high reliability, and robustness with an increase in the scale of the pipeline and with an extension of the operation time.
The appearance quality index of tobacco leaves is widely used in the tobacco industry. But in national standards for flue-cured tobacco, the specified indicators only have qualitative descriptions, and a few have a range of quantitative values, lacking quantitative calculation methods, which affects the effective use of these indicators in tobacco automatic grading. In this work, we provided a computer vision-based quantitative research approach for color intensity, length, waste, and body of tobacco appearance quality indicators. We also designed quantitative algorithms for these indices to achieve precise quantitative values to address this issue. Especially we proposed the quantization algorithm of color intensity and waste originally. In order to employ the quantification algorithm for each index, the tobacco leaf image was first segmented to determine the tobacco leaf region in the picture. Second, a mesh segmentation technique for the color intensity is developed. A comparison of the color differences between the several sub-images of the tobacco leaf image is divided. The pixel length was swiftly calculated, the boundary points at both ends of the tobacco leaf were located, the minimum outer rectangle of the tobacco leaf was calculated for the length index, and the actual length was obtained by the checkerboard reference data. Internal waste and marginal waste are the categories under which the waste index is divided. To locate holes and abnormal areas for internal waste, the connected region analysis is employed. The waveform of the edge was created and studied to determine the missing part of the edge. The actual area of the tobacco leaf was calculated by the design algorithm, the body index was expressed as weight per unit area, and the weight was determined by a pressure sensor. Finally, each index's experimental verification is designed. The empirical findings demonstrate that semantic segmentation average accuracy is 8.4% higher than threshold segmentation in the extraction of tobacco leaf regions. The average relative error between the calculated tobacco leaf length and the manual measurement is 2.83%. The average accuracy of tobacco leaf position classification was 88.52% under the six classifiers. The correlation coefficient between tobacco leaf body quantification value and tobacco leaf thickness value is 0.9270.
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