Research on Mobile Robot Navigation Method Based on Semantic Information.

This paper proposes a solution to the problem of mobile robot navigation and trajectory interpolation in dynamic environments with large scenes. The solution combines a semantic laser SLAM system that utilizes deep learning and a trajectory interpolation algorithm. The paper first introduces some open-source laser SLAM algorithms and then elaborates in detail on the general framework of the SLAM system used in this paper. Second, the concept of voxels is introduced into the occupation probability map to enhance the ability of local voxel maps to represent dynamic objects. Then, in this paper, we propose a PointNet++ point cloud semantic segmentation network combined with deep learning algorithms to extract deep features of dynamic point clouds in large scenes and output semantic information of points on static objects. A descriptor of the global environment is generated based on its semantic information. Closed-loop completion of global map optimization is performed to reduce cumulative error. Finally, T-trajectory interpolation is utilized to ensure the motion performance of the robot and improve the smooth stability of the robot trajectory. The experimental results indicate that the combination of the semantic laser SLAM system with deep learning and the trajectory interpolation algorithm proposed in this paper yields better graph-building and loop-closure effects in large scenes at SIASUN large scene campus. The use of T-trajectory interpolation ensures vibration-free and stable transitions between target points.

Mechanistic effects of microwave radiation on pupal emergence in the leafminer fly, Liriomyza trifolii.

Liriomyza trifolii is a significant pest of vegetable and ornamental crops across the globe. Microwave radiation has been used for controlling pests in stored products; however, there are few reports on the use of microwaves for eradicating agricultural pests such as L. trifolii, and its effects on pests at the molecular level is unclear. In this study, we show that microwave radiation inhibited the emergence of L. trifolii pupae. Transcriptomic studies of L. trifolii indicated significant enrichment of differentially expressed genes (DEGs) in 'post-translational modification, protein turnover, chaperones', 'sensory perception of pain/transcription repressor complex/zinc ion binding' and 'insulin signaling pathway' when analyzed with the Clusters of Orthologous Groups, Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes databases, respectively. The top DEGs were related to reproduction, immunity and development and were significantly expressed after microwave radiation. Interestingly, there was no significant difference in the expression of genes encoding heat shock proteins or antioxidant enzymes in L. trifolii treated with microwave radiation as compared to the untreated control. The expression of DEGs encoding cuticular protein and protein takeout were silenced by RNA interference, and the results showed that knockdown of these two DEGs reduced the survival of L. trifolii exposed to microwave radiation. The results of this study help elucidate the molecular response of L. trifolii exposed to microwave radiation and provide novel ideas for control.

Research on Small Sample Multi-Target Grasping Technology Based on Transfer Learning.

This article proposes a CBAM-ASPP-SqueezeNet model based on the attention mechanism and atrous spatial pyramid pooling (CBAM-ASPP) to solve the problem of robot multi-target grasping detection. Firstly, the paper establishes and expends a multi-target grasping dataset, as well as introduces and uses transfer learning to conduct network pre-training on the single-target dataset and slightly modify the model parameters using the multi-target dataset. Secondly, the SqueezeNet model is optimized and improved using the attention mechanism and atrous spatial pyramid pooling module. The paper introduces the attention mechanism network to weight the transmitted feature map in the channel and spatial dimensions. It uses a variety of parallel operations of atrous convolution with different atrous rates to increase the size of the receptive field and preserve features from different ranges. Finally, the CBAM-ASPP-SqueezeNet algorithm is verified using the self-constructed, multi-target capture dataset. When the paper introduces transfer learning, the various indicators converge after training 20 epochs. In the physical grabbing experiment conducted by Kinova and SIASUN Arm, a network grabbing success rate of 93% was achieved.

Object Recognition and Grasping for Collaborative Robots Based on Vision.

This study introduces a parallel YOLO-GG deep learning network for collaborative robot target recognition and grasping to enhance the efficiency and precision of visual classification and grasping for collaborative robots. First, the paper outlines the target classification and detection task, the grasping system of the robotic arm, and the dataset preprocessing method. The real-time recognition and grasping network can identify a diverse spectrum of unidentified objects and determine the target type and appropriate capture box. Secondly, we propose a parallel YOLO-GG deep vision network based on YOLO and GG-CNN. Thirdly, the YOLOv3 network, pre-trained with the COCO dataset, identifies the object category and position, while the GG-CNN network, trained using the Cornell Grasping dataset, predicts the grasping pose and scale. This study presents the processes for generating a target's grasping frame and recognition type using GG-CNN and YOLO networks, respectively. This completes the investigation of parallel networks for target recognition and grasping in collaborative robots. Finally, the experimental results are evaluated on the self-constructed NEU-COCO dataset for target recognition and positional grasping. The speed of detection has improved by 14.1%, with an accuracy of 94%. This accuracy is 4.0% greater than that of YOLOv3. Experimental proof was obtained through a robot grasping actual objects.

Degradation Adaption Local-to-Global Transformer for Low-Dose CT Image Denoising.

Computer tomography (CT) has played an essential role in the field of medical diagnosis. Considering the potential risk of exposing patients to X-ray radiations, low-dose CT (LDCT) images have been widely applied in the medical imaging field. Since reducing the radiation dose may result in increased noise and artifacts, methods that can eliminate the noise and artifacts in the LDCT image have drawn increasing attentions and produced impressive results over the past decades. However, recent proposed methods mostly suffer from noise remaining, over-smoothing structures, or false lesions derived from noise. To tackle these issues, we propose a novel degradation adaption local-to-global transformer (DALG-Transformer) for restoring the LDCT image. Specifically, the DALG-Transformer is built on self-attention modules which excel at modeling long-range information between image patch sequences. Meanwhile, an unsupervised degradation representation learning scheme is first developed in medical image processing to learn abstract degradation representations of the LDCT images, which can distinguish various degradations in the representation space rather than the pixel space. Then, we introduce a degradation-aware modulated convolution and gated mechanism into the building modules (i.e., multi-head attention and feed-forward network) of each Transformer block, which can bring in the complementary strength of convolution operation to emphasize on the spatially local context. The experimental results show that the DALG-Transformer can provide superior performance in noise removal, structure preservation, and false lesions elimination compared with five existing representative deep networks. The proposed networks may be readily applied to other image processing tasks including image reconstruction, image deblurring, and image super-resolution.

Transfer RNA-derived fragment 5'tRF-Gly promotes the development of hepatocellular carcinoma by direct targeting of carcinoembryonic antigen-related cell adhesion molecule 1.

Transfer RNA-derived fragments are a group of small noncoding single-stranded RNA that play essential roles in multiple diseases. However, their biological functions in carcinogenesis are not well understood. In this study, 5'tRF-Gly was found to have significantly high expression in hepatocellular carcinoma (HCC), and the upregulation of 5'tRF-Gly was positively correlated with tumor size and tumor metastasis. Overexpression of 5'tRF-Gly induced increased growth rate and metastasis in HCC cells in vitro and in nude mice, while knockdown showed the opposite effect. Carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM1) was confirmed to be a direct target of 5'tRF-Gly in HCC. In addition, the cytological effect of CEACAM1 knockdown proved to be similar to the overexpression of 5'tRF-Gly. Moreover, attenuation of CEACAM1 expression rescued the 5'tRF-Gly-mediated promoting effects on HCC cells. These data show that 5'tRF-Gly is a new tumor-promoting factor and could be a potential diagnostic biomarker or new therapeutic target for HCC.

Insect hormones affect the toxicity of the insecticidal growth regulator cyromazine in Liriomyza trifolii (Diptera: Agromyzidae).

The leafminer Liriomyza trifolii is an important insect pest of ornamental and vegetable crops worldwide. Cyromazine is an effective, commonly-used insecticide that functions as a growth regulator, but its effect on L. trifolii has not been previously reported. In this study, transcriptome analysis was undertaken in L. trifolii exposed to cyromazine. Clusters of orthologous groups analysis indicated that a large number of differentially expressed genes responding to cyromazine were categorized as "lipid transport and metabolism", "post-translational modification, protein turnover, chaperones", and "cell wall/membrane/envelope biogenesis". Gene ontology analysis indicated that pathways associated with insect hormones, growth and development, and cuticle synthesis were significantly enriched. In general, the transcriptome results showed that the genes related to insect hormones were significantly expressed after treatment with cyromazine. Furthermore, the combined exposure of L. trifolii to cyromazine and the hormone analogues 20-hydroxyecdysone (20E) or juvenile hormone (JH) indicated that hormone analogues can change the expression pattern of hormone-related genes (20EP and JHEH) and pupal length. The combined application of cyromazine with 20E improved the survival rate of L. trifolii, whereas the combination of JH and cyromazine reduced survival. The results of this study help elucidate the mechanistic basis for cyromazine toxicity and provide a foundation for understanding cyromazine resistance.

An Efficient Hierarchical Optic Disc and Cup Segmentation Network Combined with Multi-task Learning and Adversarial Learning.

Automatic and accurate segmentation of optic disc (OD) and optic cup (OC) in fundus images is a fundamental task in computer-aided ocular pathologies diagnosis. The complex structures, such as blood vessels and macular region, and the existence of lesions in fundus images bring great challenges to the segmentation task. Recently, the convolutional neural network-based methods have exhibited its potential in fundus image analysis. In this paper, we propose a cascaded two-stage network architecture for robust and accurate OD and OC segmentation in fundus images. In the first stage, the U-Net like framework with an improved attention mechanism and focal loss is proposed to detect accurate and reliable OD location from the full-scale resolution fundus images. Based on the outputs of the first stage, a refined segmentation network in the second stage that integrates multi-task framework and adversarial learning is further designed for OD and OC segmentation separately. The multi-task framework is conducted to predict the OD and OC masks by simultaneously estimating contours and distance maps as auxiliary tasks, which can guarantee the smoothness and shape of object in segmentation predictions. The adversarial learning technique is introduced to encourage the segmentation network to produce an output that is consistent with the true labels in space and shape distribution. We evaluate the performance of our method using two public retinal fundus image datasets (RIM-ONE-r3 and REFUGE). Extensive ablation studies and comparison experiments with existing methods demonstrate that our approach can produce competitive performance compared with state-of-the-art methods.

MID-UNet: Multi-input directional UNet for COVID-19 lung infection segmentation from CT images.

Coronavirus Disease 2019 (COVID-19) has spread globally since the first case was reported in December 2019, becoming a world-wide existential health crisis with over 90 million total confirmed cases. Segmentation of lung infection from computed tomography (CT) scans via deep learning method has a great potential in assisting the diagnosis and healthcare for COVID-19. However, current deep learning methods for segmenting infection regions from lung CT images suffer from three problems: (1) Low differentiation of semantic features between the COVID-19 infection regions, other pneumonia regions and normal lung tissues; (2) High variation of visual characteristics between different COVID-19 cases or stages; (3) High difficulty in constraining the irregular boundaries of the COVID-19 infection regions. To solve these problems, a multi-input directional UNet (MID-UNet) is proposed to segment COVID-19 infections in lung CT images. For the input part of the network, we firstly propose an image blurry descriptor to reflect the texture characteristic of the infections. Then the original CT image, the image enhanced by the adaptive histogram equalization, the image filtered by the non-local means filter and the blurry feature map are adopted together as the input of the proposed network. For the structure of the network, we propose the directional convolution block (DCB) which consist of 4 directional convolution kernels. DCBs are applied on the short-cut connections to refine the extracted features before they are transferred to the de-convolution parts. Furthermore, we propose a contour loss based on local curvature histogram then combine it with the binary cross entropy (BCE) loss and the intersection over union (IOU) loss for better segmentation boundary constraint. Experimental results on the COVID-19-CT-Seg dataset demonstrate that our proposed MID-UNet provides superior performance over the state-of-the-art methods on segmenting COVID-19 infections from CT images.

Inhibitory effect of activin A on IL-9 production by mouse NK cells through Smad3 signaling.

Interleukin-9 (IL-9) is a cytokine secreted by T-helper (Th)9 cells, and activin A can enhance Th9 cell differentiation. However, whether activin A affects IL-9 production by natural killer (NK) cells remains unclear. Herein, we found that not only Th cells, but also CD3-CD49b+NKp46+ NK cells of Balb/c mice produced IL-9. Although activin A promoted IL-9 expression in CD4+ Th cells, it inhibited IL-9 production by CD49b+NKp46+ NK cells in mice. Furthermore, the enzyme-linked immunosorbent assay (ELISA) results showed that mouse NK cells could secrete mature IL-9 protein, and activin A inhibited IL-9 release by NK cells. Additionally, activin A inhibited interferon (IFN)-γ production in splenic NK cells in mice, but promoted IL-2 production, and did not alter the production of IL-10. Western blotting results showed that levels of activin type IIA receptor (ActRIIA), Smad3 and phosphorylated-Smad3 (p-SMAD3) protein increased in activin A-treated splenic NK cells, compared with that in control NK cells. The inhibitory effects of activin A on IL-9 production by NK cells were attenuated in the presence of activin antagonist follistatin (FST) or Smad3 knockdown to NK cells. These data suggest that although activin A up-regulates IL-9 expression in Th cells, it inhibits IL-9 production in NK cells through Smad3 signaling.

A Novel Pulmonary Nodule Detection Model Based on Multi-Step Cascaded Networks.

Pulmonary nodule detection in chest computed tomography (CT) is of great significance for the early diagnosis of lung cancer. Therefore, it has attracted more and more researchers to propose various computer-assisted pulmonary nodule detection methods. However, these methods still could not provide convincing results because the nodules are easily confused with calcifications, vessels, or other benign lumps. In this paper, we propose a novel deep convolutional neural network (DCNN) framework for detecting pulmonary nodules in the chest CT image. The framework consists of three cascaded networks: First, a U-net network integrating inception structure and dense skip connection is proposed to segment the region of lung parenchyma from the chest CT image. The inception structure is used to replace the first convolution layer for better feature extraction with respect to multiple receptive fields, while the dense skip connection could reuse these features and transfer them through the network. Secondly, a modified U-net network where all the convolution layers are replaced by dilated convolution is proposed to detect the "suspicious nodules" in the image. The dilated convolution can increase the receptive fields to improve the ability of the network in learning global information of the image. Thirdly, a modified U-net adapting multi-scale pooling and multi-resolution convolution connection is proposed to find the true pulmonary nodule in the image with multiple candidate regions. During the detection, the result of the former step is used as the input of the latter step to follow the "coarse-to-fine" detection process. Moreover, the focal loss, perceptual loss and dice loss were used together to replace the cross-entropy loss to solve the problem of imbalance distribution of positive and negative samples. We apply our method on two public datasets to evaluate its ability in pulmonary nodule detection. Experimental results illustrate that the proposed method outperform the state-of-the-art methods with respect to accuracy, sensitivity and specificity.

Collaborative Neural Network Algorithm for Event-Driven Deployment in Wireless Sensor and Robot Networks.

Wireless sensor and robot networks (WSRNs) often work in complex and dangerous environments that are subject to many constraints. For obtaining a better monitoring performance, it is necessary to deploy different types of sensors for various complex environments and constraints. The traditional event-driven deployment algorithm is only applicable to a single type of monitoring scenario, so cannot effectively adapt to different types of monitoring scenarios at the same time. In this paper, a multi-constrained event-driven deployment model is proposed based on the maximum entropy function, which transforms the complex event-driven deployment problem into two continuously differentiable single-objective sub-problems. Then, a collaborative neural network (CONN) event-driven deployment algorithm is proposed based on neural network methods. The CONN event-driven deployment algorithm effectively solves the problem that it is difficult to obtain a large amount of sensor data and environmental information in a complex and dangerous monitoring environment. Unlike traditional deployment methods, the CONN algorithm can adaptively provide an optimal deployment solution for a variety of complex monitoring environments. This greatly reduces the time and cost involved in adapting to different monitoring environments. Finally, a large number of experiments verify the performance of the CONN algorithm, which can be adapted to a variety of complex application scenarios.

Computed Tomography (CT) Image Quality Enhancement via a Uniform Framework Integrating Noise Estimation and Super-Resolution Networks.

Computed tomography (CT) imaging technology has been widely used to assist medical diagnosis in recent years. However, noise during the process of imaging, and data compression during the process of storage and transmission always interrupt the image quality, resulting in unreliable performance of the post-processing steps in the computer assisted diagnosis system (CADs), such as medical image segmentation, feature extraction, and medical image classification. Since the degradation of medical images typically appears as noise and low-resolution blurring, in this paper, we propose a uniform deep convolutional neural network (DCNN) framework to handle the de-noising and super-resolution of the CT image at the same time. The framework consists of two steps: Firstly, a dense-inception network integrating an inception structure and dense skip connection is proposed to estimate the noise level. The inception structure is used to extract the noise and blurring features with respect to multiple receptive fields, while the dense skip connection can reuse those extracted features and transfer them across the network. Secondly, a modified residual-dense network combined with joint loss is proposed to reconstruct the high-resolution image with low noise. The inception block is applied on each skip connection of the dense-residual network so that the structure features of the image are transferred through the network more than the noise and blurring features. Moreover, both the perceptual loss and the mean square error (MSE) loss are used to restrain the network, leading to better performance in the reconstruction of image edges and details. Our proposed network integrates the degradation estimation, noise removal, and image super-resolution in one uniform framework to enhance medical image quality. We apply our method to the Cancer Imaging Archive (TCIA) public dataset to evaluate its ability in medical image quality enhancement. The experimental results demonstrate that the proposed method outperforms the state-of-the-art methods on de-noising and super-resolution by providing higher peak signal to noise ratio (PSNR) and structure similarity index (SSIM) values.

The Repair Strategy for Event Coverage Holes Based on Mobile Robots in Wireless Sensor and Robot Networks.

In the application of the wireless sensor and robot networks (WSRNs), there is an urgent need to accommodate flexible surveillance tasks in intricate surveillance scenarios. On the condition of flexible surveillance missions and demands, event coverage holes occur in the networks. The conventional network repair methods based on the geometric graph theory such as Voronoi diagram method are unable to meet the conditions of flexible surveillance tasks and severe multi-restraint scenarios. Mobile robots show obvious advantages in terms of adaptation capacity and mobility in hazardous and severe scenarios. First, we propose an event coverage hole healing model for multi-constrained scenarios. Then, we propose a joint event coverage hole repair algorithm (JECHR) on the basis of global repair and local repair to apply mobile robots to heal event coverage holes in WSRNs. Different from conventional healing methods, the proposed algorithm can heal event coverage holes efficaciously which are resulted from changing surveillance demands and scenarios. The JECHR algorithm can provide an optimal repair method, which is able to adapt different kinds of severe multi-constrained circumstances. Finally, a large number of repair simulation experiments verify the performance of the JECHR algorithm which can be adapted to a variety of intricate surveillance tasks and application scenarios.

A 3D Coverage Algorithm Based on Complex Surfaces for UAVs in Wireless Multimedia Sensor Networks.

Following the development of wireless multimedia sensor networks (WMSN), the coverage of the sensors in the network constitutes one of the key technologies that have a significant influence on the monitoring ability, quality of service, and network lifetime. The application environment of WMSN is always a complex surface, such as a hilly surface, that would likely cause monitoring shadowing problems. In this study, a new coverage-enhancing algorithm is presented to achieve an optimal coverage ratio of WMSN based on three-dimensional (3D) complex surfaces. By aiming at the complex surface, the use of a 3D sensing model, including a sensor monitoring model and a surface map calculation algorithm, is proposed to calculate the WMSN coverage information in an accurate manner. The coverage base map allowed the efficient estimation of the degree of monitoring occlusion efficiently and improved the system's accuracy. To meet the requests of complex 3D surface monitoring tasks for multiple sensors, we propose a modified cuckoo search algorithm that considers the features of the WMSN coverage problem and combines the survival of the fittest, dynamic discovery probability, and the self-adaptation strategy of rotation. The evaluation outcomes demonstrate that the proposed algorithm can describe the 3D covering field but also improve both the coverage quality and efficiency of the WMSN on a complex surface.

Barrier Coverage for 3D Camera Sensor Networks.

Barrier coverage, an important research area with respect to camera sensor networks, consists of a number of camera sensors to detect intruders that pass through the barrier area. Existing works on barrier coverage such as local face-view barrier coverage and full-view barrier coverage typically assume that each intruder is considered as a point. However, the crucial feature (e.g., size) of the intruder should be taken into account in the real-world applications. In this paper, we propose a realistic resolution criterion based on a three-dimensional (3D) sensing model of a camera sensor for capturing the intruder's face. Based on the new resolution criterion, we study the barrier coverage of a feasible deployment strategy in camera sensor networks. Performance results demonstrate that our barrier coverage with more practical considerations is capable of providing a desirable surveillance level. Moreover, compared with local face-view barrier coverage and full-view barrier coverage, our barrier coverage is more reasonable and closer to reality. To the best of our knowledge, our work is the first to propose barrier coverage for 3D camera sensor networks.

Shape Reconstruction Based on a New Blurring Model at the Micro/Nanometer Scale.

Real-time observation of three-dimensional (3D) information has great significance in nanotechnology. However, normal nanometer scale observation techniques, including transmission electron microscopy (TEM), and scanning probe microscopy (SPM), have some problems to obtain 3D information because they lack non-destructive, intuitive, and fast imaging ability under normal conditions, and optical methods have not widely used in micro/nanometer shape reconstruction due to the practical requirements and the imaging limitations in micro/nano manipulation. In this paper, a high resolution shape reconstruction method based on a new optical blurring model is proposed. Firstly, the heat diffusion physics equation is analyzed and the optical diffraction model is modified to directly explain the basic principles of image blurring resulting from depth variation. Secondly, a blurring imaging model is proposed based on curve fitting of a 4th order polynomial curve. The heat diffusion equations combined with the blurring imaging are introduced, and their solution is transformed into a dynamic optimization problem. Finally, the experiments with a standard nanogrid, an atomic force microscopy (AFM) cantilever and a microlens have been conducted. The experiments prove that the proposed method can reconstruct 3D shapes at the micro/nanometer scale, and the minimal reconstruction error is 3 nm.

Compound Event Barrier Coverage in Wireless Sensor Networks under Multi-Constraint Conditions.

It is important to monitor compound event by barrier coverage issues in wireless sensor networks (WSNs). Compound event barrier coverage (CEBC) is a novel coverage problem. Unlike traditional ones, the data of compound event barrier coverage comes from different types of sensors. It will be subject to multiple constraints under complex conditions in real-world applications. The main objective of this paper is to design an efficient algorithm for complex conditions that can combine the compound event confidence. Moreover, a multiplier method based on an active-set strategy (ASMP) is proposed to optimize the multiple constraints in compound event barrier coverage. The algorithm can calculate the coverage ratio efficiently and allocate the sensor resources reasonably in compound event barrier coverage. The proposed algorithm can simplify complex problems to reduce the computational load of the network and improve the network efficiency. The simulation results demonstrate that the proposed algorithm is more effective and efficient than existing methods, especially in the allocation of sensor resources.

Diffusion-based three-dimensional reconstruction of complex surface using monocular vision.

Three-dimensional (3D) reconstruction based on optical diffusion has certain significant advantages, such as its capacity for high-precision depth estimation with a small lens, distant-object depth estimation, a monocular vision basis, and no required camera or scene adjustment. However, few mathematical models to relate the depth information acquired using this technique to the basic principles of intensity distribution during optical diffusion have been proposed. In this paper, the heat diffusion equation of physics is applied in order to construct a mathematical model of the intensity distribution during optical diffusion. Hence, a high-precision 3D reconstruction method with optical diffusion based on the heat diffusion equation is proposed. First, the heat diffusion equation is analyzed and an optical diffusion model is introduced to explain the basic principles of the diffusion imaging process. Second, the novel 3D reconstruction method based on global heat diffusion is proposed, which incorporates the relationship between the depth information and the degree of diffusion. Finally, a simulation involving synthetic images and an experiment using five playing cards are conducted, with the results confirming the effectiveness and feasibility of the proposed method.

The Indoor Localization and Tracking Estimation Method of Mobile Targets in Three-Dimensional Wireless Sensor Networks.

Indoor localization is a significant research area in wireless sensor networks (WSNs). Generally, the nodes of WSNs are deployed in the same plane, i.e., the floor, as the target to be positioned, which causes the sensing signal to be influenced or even blocked by unpredictable obstacles, like furniture. However, a 3D system, like Cricket, can reduce the negative impact of obstacles to the maximum extent and guarantee the sensing signal transmission by using the line of sight (LOS). However, most of the traditional localization methods are not available for the new deployment mode. In this paper, we propose the self-localization of beacons method based on the Cayley-Menger determinant, which can determine the positions of beacons stuck in the ceiling; and differential sensitivity analysis (DSA) is also applied to eliminate measurement errors in measurement data fusion. Then, the calibration of beacons scheme is proposed to further refine the locations of beacons by the mobile robot. According to the robot's motion model based on dead reckoning, which is the process of determining one's current position, we employ the H∞ filter and the strong tracking filter (STF) to calibrate the rough locations, respectively. Lastly, the optimal node selection scheme based on geometric dilution precision (GDOP) is presented here, which is able to pick the group of beacons with the minimum GDOP from all of the beacons. Then, we propose the GDOP-based weighting estimation method (GWEM) to associate redundant information with the position of the target. To verify the proposed methods in the paper, we design and conduct a simulation and an experiment in an indoor setting. Compared to EKF and the H∞ filter, the adopted STF method can more effectively calibrate the locations of beacons; GWEM can provide centimeter-level precision in 3D environments by using the combination of beacons that minimizes GDOP.