PLDS-SLAM: Point and Line Features SLAM in Dynamic Environment

Visual simultaneous localization and mapping (SLAM), based on point features, achieves high localization accuracy and map construction. They primarily perform simultaneous localization and mapping based on static features. Despite their efficiency and high precision, they are prone to instability and even failure in complex environments. In a dynamic environment, it is easy to keep track of failures and even failures in work. The dynamic object elimination method, based on semantic segmentation, often recognizes dynamic objects and static objects without distinction. If there are many semantic segmentation objects or the distribution of segmentation objects is uneven in the camera view, this may result in feature offset and deficiency for map matching and motion tracking, which will lead to problems, such as reduced system accuracy, tracking failure, and track loss. To address these issues, we propose a novel point-line SLAM system based on dynamic environments. The method we propose obtains the prior dynamic region features by detecting and segmenting the dynamic region. It realizes the separation of dynamic and static objects by proposing a geometric constraint method for matching line segments, combined with the epipolar constraint method of feature points. Additionally, a dynamic feature tracking method based on Bayesian theory is proposed to eliminate the dynamic noise of points and lines and improve the robustness and accuracy of the SLAM system. We have performed extensive experiments on the KITTI and HPatches datasets to verify these claims. The experimental results show that our proposed method has excellent performance in dynamic and complex scenes

A Pseudoinverse Siamese Convolutional Neural Network of Transformation Invariance Feature Detection and Description for a SLAM System

Simultaneous localization and mapping (SLAM) systems play an important role in the field of automated robotics and artificial intelligence. Feature detection and matching are crucial aspects affecting the overall accuracy of the SLAM system. However, the accuracy of the position and matching cannot be guaranteed when confronted with a cross-view angle, illumination, texture, etc. Moreover, deep learning methods are very sensitive to perspective change and do not have the invariance of geometric transformation. Therefore, a novel pseudo-Siamese convolutional network of a transformation invariance feature detection and a description for the SLAM system is proposed in this paper. The proposed method, by learning transformation invariance features and descriptors, simultaneously improves the front-end landmark detection and tracking module of the SLAM system. We converted the input image to the transform field; the backbone network was designed to extract feature maps. Then, the feature detection subnetwork and feature description subnetwork were decomposed and designed; finally, we constructed a convolutional network of transformation invariance feature detections and a description for the visual SLAM system. We implemented many experiments in datasets, and the results of the experiments demonstrated that our method has a state-of-the-art performance in global tracking when compared to that of the traditional visual SLAM systems

A Pseudoinverse Siamese Convolutional Neural Network of Transformation Invariance Feature Detection and Description for a SLAM System

Simultaneous localization and mapping (SLAM) systems play an important role in the field of automated robotics and artificial intelligence. Feature detection and matching are crucial aspects affecting the overall accuracy of the SLAM system. However, the accuracy of the position and matching cannot be guaranteed when confronted with a cross-view angle, illumination, texture, etc. Moreover, deep learning methods are very sensitive to perspective change and do not have the invariance of geometric transformation. Therefore, a novel pseudo-Siamese convolutional network of a transformation invariance feature detection and a description for the SLAM system is proposed in this paper. The proposed method, by learning transformation invariance features and descriptors, simultaneously improves the front-end landmark detection and tracking module of the SLAM system. We converted the input image to the transform field; the backbone network was designed to extract feature maps. Then, the feature detection subnetwork and feature description subnetwork were decomposed and designed; finally, we constructed a convolutional network of transformation invariance feature detections and a description for the visual SLAM system. We implemented many experiments in datasets, and the results of the experiments demonstrated that our method has a state-of-the-art performance in global tracking when compared to that of the traditional visual SLAM systems

Cross-Viewpoint Template Matching Based on Heterogeneous Feature Alignment and Pixel-Wise Consensus for Air- and Space-Based Platforms

Template matching is the fundamental task in remote sensing image processing of air- and space-based platforms. Due to the heterogeneous image sources, different scales and different viewpoints, the realization of a general end-to-end matching model is still a challenging task. Considering the abovementioned problems, we propose a cross-view remote sensing image matching method. Firstly, a spatial attention map was proposed to solve the problem of the domain gap. It is produced by two-dimensional Gaussian distribution and eliminates the distance between the distributed heterogeneous features. Secondly, in order to perform matching at different flight altitudes, a multi-scale matching method was proposed to perform matching on three down-sampling scales in turn and confirm the optimal result. Thirdly, to improve the adaptability of the viewpoint changes, a pixel-wise consensus method based on a correlation layer was applied. Finally, we trained the proposed model based on weakly supervised learning, which does not require extensive annotation but only labels one pair of feature points of the template image and search image. The robustness and effectiveness of the proposed methods were demonstrated by evaluation on various datasets. Our method accommodates three types of template matching with different viewpoints, including SAR to RGB, infrared to RGB, and RGB to RGB

Reliable Design and Control Implementation of Parallel DC/DC Converter for High Power Charging System

With the current popularity of Electric Vehicles (EV), especially in some critical EV applications such as hospital EV fleets, the demand for continuous and reliable power supply is increasing. However, most of the charging stations are powered in a centralized way, which causes transistors and other components to be subjected to high voltage and current stresses that reduce reliability, and a single point of failure can cause the entire system to fail. Therefore, a significant effort is made in this paper to improve the reliability of the charging system. In terms of charging system structure design, a distributed charging structure with fault tolerance is designed and a mathematical model to evaluate the reliability of the structure is proposed. In terms of control, a current sharing control algorithm is designed that can achieve fault tolerance. To further improve the reliability of the system, a thermal sharing control method based on current sharing technology is also designed. This method can improve the reliability of the charging system by distributing the load more rationally according to the differences in component performance and operating environment; FPGA-based control techniques are provided, and innovative ideas of pipeline control and details of mathematical reasoning for key IP cores are presented. Experiments show that N + 1 redundancy fault tolerance can be achieved in both current sharing and thermal sharing modes. In the current sharing experiment, when module 3 failed, the total current only fluctuated 800 mA within 500 ms, which is satisfactory. In the thermal sharing experiment, after module 3 failed, modules 1, 2, and 4 adjusted the current reasonably under the correction of the thermal sharing loop, and the total current remained stable throughout the process. The experimental results prove that the charging system structure design and control method proposed in this paper are feasible and excellent

Reliable Design and Control Implementation of Parallel DC/DC Converter for High Power Charging System

With the current popularity of Electric Vehicles (EV), especially in some critical EV applications such as hospital EV fleets, the demand for continuous and reliable power supply is increasing. However, most of the charging stations are powered in a centralized way, which causes transistors and other components to be subjected to high voltage and current stresses that reduce reliability, and a single point of failure can cause the entire system to fail. Therefore, a significant effort is made in this paper to improve the reliability of the charging system. In terms of charging system structure design, a distributed charging structure with fault tolerance is designed and a mathematical model to evaluate the reliability of the structure is proposed. In terms of control, a current sharing control algorithm is designed that can achieve fault tolerance. To further improve the reliability of the system, a thermal sharing control method based on current sharing technology is also designed. This method can improve the reliability of the charging system by distributing the load more rationally according to the differences in component performance and operating environment; FPGA-based control techniques are provided, and innovative ideas of pipeline control and details of mathematical reasoning for key IP cores are presented. Experiments show that N + 1 redundancy fault tolerance can be achieved in both current sharing and thermal sharing modes. In the current sharing experiment, when module 3 failed, the total current only fluctuated 800 mA within 500 ms, which is satisfactory. In the thermal sharing experiment, after module 3 failed, modules 1, 2, and 4 adjusted the current reasonably under the correction of the thermal sharing loop, and the total current remained stable throughout the process. The experimental results prove that the charging system structure design and control method proposed in this paper are feasible and excellent

Optimization of a Nanofiltration Desalination Process for Antarctic Krill Peptides Using Orthogonal Tests

Antarctic krill (Euphausia superba), an important group of marine zooplankton in the Southern Ocean, is the only fishery resource with extremely rich reserves and a low degree of development in the world. Antarctic krill is considered to be the greatest potential source of high-quality marine protein resources due to its abundant biomass and high protein content. Peptides prepared from Antarctic krill exhibit multiple physiological activities, including osteoporosis relief, glucose metabolism regulation, blood pressure amelioration, antioxidation, fatigue alleviation, and anti-aging activity. The production and development of Antarctic krill peptides has recently become an industry focus; however, existing research has been limited to the optimization of enzymatic hydrolysis processes, mainly involving the screening of suitable enzymes and the optimization of enzymatic hydrolysis conditions. Due to the high mineral content of Antarctic krill and the introduction of buffer salt in the process of enzymatic hydrolysis, current Antarctic krill peptides products have a high salt content, which leads to poor sensory experience and health risks. Hence, a process for desalination of Antarctic krill peptides is needed. Desalination methods for bioactive substances include dialysis, ultrafiltration, nanofiltration, electrodialysis, and macroporous resin adsorption. In the field of membrane separation, nanofiltration technology has been widely used in the purification, concentration, and desalination of food components owing to its advantages: low operation cost, no introduction of exogenous substances, no destruction of materials, and low rejection rate of monovalent ions. In order to improve product quality and ensure market expansion, the process of desalination of Antarctic krill peptides using nanofiltration technology was studied and optimized in this study.Defatted Antarctic krill powder was enzymatically hydrolyzed by alkaline protease to obtain Antarctic krill peptides for further use. The main factors affecting the desalination effect of Antarctic krill peptides (peptides concentration, nanofiltration pressure, and cycle times) were optimized by single-factor and orthogonal tests, using the desalination rate and protein loss rate as evaluation indexes. The experimental optimization ranges included peptides concentration of 1%~5%, nanofiltration pressure of 0.6~1.4 MPa and cycle times of 1~5. The salt contents of the samples before and after desalination were quantified using the silver nitrate titration method; the protein contents of the experimental samples were quantified using the Lowry colorimetric method. The quality indexes of the Antarctic krill peptides after treatment (including the basic nutritional composition: moisture content, protein content, ash content, salt content; amino acid composition; and molecular weight distribution) were systematically evaluated by the corresponding national standard methods. All experiments were performed in triplicate, and data were expressed as mean ± standard deviation. Excel 2016, IBM SPSS 20.0, and Origin 2018 were used for data analysis and chart drawing.Single-factor tests revealed that peptides concentration of 3%, nanofiltration pressure of 1.0 MPa and a cycle time of 2 could be selected as the design basis for the L9 (33) orthogonal test. The range value of the orthogonal test indicated that the degree of influence of the three factors on the desalination effect was as follows: peptides concentration > cycle times > nanofiltration pressure. The optimum conditions for desalting Antarctic krill peptides obtained by k value analysis were as follows: peptides concentration of 3.0%, nanofiltration pressure of 1.2 MPa and a cycle time of 3. Under the optimal condition, the desalination rate of the Antarctic krill peptides reached up to (86.35±2.11)%, and the protein loss rate was controlled at (9.10±0.35)%, demonstrating the feasibility of the process. The salt content of the Antarctic krill peptides after desalination was reduced to (1.14±0.12)% and the protein content was (92.73±2.29)%. The molecular weights of the Antarctic krill peptides after desalination were mainly distributed between 189 Da and 6500 Da, of which the proportion of peptides with molecular weight less than 3000 Da was (88.91±2.19)%, conforming to the molecular weight distribution range of bioactive peptides. The amount of essential amino acids in the Antarctic krill peptides after desalination accounted for (40.06±0.10)% of the total amino acids, and the ratio of essential amino acids to nonessential amino acids was (66.82±0.28)%. The amino acid compositions of the Antarctic krill peptides after desalination were ideal and met the standard stipulated by the FAO/WHO. The established nanofiltration desalination process presented good treatment effects, and the obtained peptides were of good quality and high nutritional value.The production of Antarctic krill protein-related products may be the next key development for the processing industry, since the sole high-value products of Antarctic krill at present are Antarctic krill oil and its derivatives. The established nanofiltration desalination process has practical application value and would provide technical support for the development of high-quality Antarctic krill peptides. This research provides scientific support for the efficient utilization of Antarctic krill resources