Trophoblast-derived miR-410-5p induces M2 macrophage polarization and mediates immunotolerance at the fetal-maternal interface by targeting the STAT1 signaling pathway.

BACKGROUND: Macrophages phenotypic deviation and immune imbalance play vital roles in pregnancy-associated diseases such as spontaneous miscarriage. Trophoblasts regulate phenotypic changes in macrophages, however, their underlying mechanism during pregnancy remains unclear. Therefore, this study aimed to elucidate the potential function of trophoblast-derived miRNAs (miR-410-5p) in macrophage polarization during pregnancy. METHODS: Patient decidual macrophage tissue samples in spontaneous abortion group and normal pregnancy group (those who had induced abortion for non-medical reasons) were collected at the Reproductive Medicine Center of Renmin Hospital of Wuhan University from April to December 2021. Furthermore, placental villi and decidua tissue samples were collected from patients who had experienced a spontaneous miscarriage and normal pregnant women for validation and subsequent experiments at the Shenzhen Zhongshan Obstetrics & Gynecology Hospital (formerly Shenzhen Zhongshan Urology Hospital), from March 2021 to September 2022. As an animal model, 36 female mice were randomly divided into six groups as follows: naive-control, lipopolysaccharide-model, agomir-negative control prevention, agomir-410-5p prevention, agomir-negative control treatment, and agomir-410-5p treatment groups. We analyzed the miR-410-5p expression in abortion tissue and plasma samples; and supplemented miR-410-5p to evaluate embryonic absorption in vivo. The main source of miR-410-5p at the maternal-fetal interface was analyzed, and the possible target gene, signal transducer and activator of transcription (STAT) 1, of miR-410-5p was predicted. The effect of miR-410-5p and STAT1 regulation on macrophage phenotype, oxidative metabolism, and mitochondrial membrane potential was analyzed in vitro. RESULTS: MiR-410-5p levels were lower in the spontaneous abortion group compared with the normal pregnancy group, and plasma miR-410-5p levels could predict pregnancy and spontaneous abortion. Prophylactic supplementation of miR-410-5p in pregnant mice reduced lipopolysaccharide-mediated embryonic absorption and downregulated the decidual macrophage pro-inflammatory phenotype. MiR-410-5p were mainly distributed in villi, and trophoblasts secreted exosomes-miR-410-5p at the maternal-fetal interface. After macrophages captured exosomes, the cells shifted to the tolerance phenotype. STAT1 was a potential target gene of miR-410-5p. MiR-410-5p bound to STAT1 mRNA, and inhibited the expression of STAT1 protein. STAT1 can drive macrophages to mature to a pro-inflammatory phenotype. MiR-410-5p competitive silencing of STAT1 can avoid macrophage immune disorders. CONCLUSION: MiR-410-5p promotes M2 macrophage polarization by inhibiting STAT1, thus ensuring a healthy pregnancy. These findings are of great significance for diagnosing and preventing spontaneous miscarriage, providing a new perspective for further research in this field.

[Comparison of Pb2+ Adsorption Properties of Biochars Modified Through CO2 Atmosphere Pyrolysis and Nitric Acid].

Acid modification has been widely used to modify the structural properties of biochars. However, acid modification led to the large consumption of acid, increased difficulty of waste effluent disposal, and a high application cost. To evaluate the advantages and application potential of biochars prepared under CO2, utilizing pyrolysis to directly modify biochars to improve heavy metal removal efficiency and reduce production cost, would be an important prerequisite for the broad application of biochars. The sorption performance of Pb2+ with CO2-modified biochars was compared with that of HNO3-modified biochar. The elemental compositions and structural properties of biochars were characterized through elemental analysis, Fourier transform infrared spectroscopy, and X-ray photoelectron spectroscopy. The results revealed that for biochars produced at 500℃, HNO3 modification produced abundant carboxylic groups and -NO2 (asy) and -NO2 (sym) groups, promoting the surface activities and complexing abilities of biochars. The CO2-modified biochars contained abundant carbonate minerals, which could remove Pb2+ by electrostatic ion exchange and coprecipitation or complex. In addition, compared to that of HNO3-modified biochars, CO2-modified biochars had the larger specific surface area and better microporous structures, which were beneficial to the diffusion of Pb2+ and further promoted surface sorption. CO2 modification increased the maximum Pb2+ sorption capacity of W500CO2 and W700CO2, which were 60.14 mg·g-1 and 71.69 mg·g-1. By contrast, HNO3-modified biochars W500N2-A and W700N2-A showed the lower Pb2+ sorption capacities, which were 42.26 mg·g-1 and 68.3 mg·g-1, respectively. The increasing of the specific surface area and functional groups simultaneously promoted the sorption capacity of CO2-modified biochars. Consequently, the CO2-modified biochar had the advantages of low cost, environmental friendliness, and high heavy metal removal efficiency, which is a modification method worthy of promotion and application.

Case report outcome of cetuximab treatment in a metastatic colorectal cancer patient with novel KRAS P34R.

Cetuximab [the epidermal growth factor receptor (EGFR)-targeting mAb] improves clinical outcomes when added to standard chemotherapy used in the treatment of metastatic colorectal cancer. Patients with hotspot mutations in Kirsten rat sarcoma viral oncogene ( KRAS ) mutation in exon 2 were not recommended to be treated with cetuximab. However, there is still a lack of clinical data for those unreported non-hotspot KRAS mutations in exon 2 and their response to cetuximab. In this study, we reported a 35-year-old woman who was diagnosed with stage IVA CRC with liver metastases. An exceptionally uncommon KRASP34R mutation in KRAS exon 2 was detected in tumor specimens by next-generation sequencing. This patient obtained limited benefit from first-line chemotherapy and did not respond to cetuximab in the second-line course. In the third-line course, the patient also did not respond to the combination treatment of furaquitinib and cindilimab. The patient died 8 months after treatment initiation. In this study, we found amplification of the rare oncogenic KRASP34R was not only associated with an aggressive phenotype, but also supported cancer resistance to cetuximab, chemotherapy, and immunotherapy.

Obesity-induced inflammatory miR-133a mediates apoptosis of granulosa cells and causes abnormal folliculogenesis.

Obesity has been reported to promote disordered folliculogenesis, but the exact molecular mechanisms are still not fully understood. In this study, we find that miR-133a is involved in obesity-induced follicular development disorder. After feeding with a high-fat diet (HFD) and fructose water for nine weeks, the mouse body weight is significantly increased, accompanied by an inflammatory state and increased expression of miR-133a in the adipose tissues and ovaries as well as accelerated follicle depletion. Although miR-133a is increased in the fat and ovaries of HFD mice, the increased miR-133a in the HFD ovaries is not derived from exosome transferred from obese adipose tissues but is synthesized by ovarian follicular cells in response to HFD-induced inflammation. In vivo experiments show that intrabursal injection of miR-133a agomir induces a decrease in primordial follicles and an increase in antral follicles and atretic follicles, which is similar to HFD-induced abnormal folliculogenesis. Overexpression of miR-133a modestly promotes granulosa cell apoptosis by balancing the expression of anti-apoptotic proteins such as C1QL1 and XIAP and pro-apoptotic proteins such as PTEN. Overall, this study reveals the function of miR-133a in obesity-induced ovarian folliculogenesis dysfunction and sheds light on the etiology of female reproductive disorders.

Indoor Passive Visual Positioning by CNN-Based Pedestrian Detection.

Indoor positioning applications are developing at a rapid pace; active visual positioning is one method that is applicable to mobile platforms. Other methods include Wi-Fi, CSI, and PDR approaches; however, their positioning accuracy usually cannot achieve the positioning performance of the active visual method. Active visual users, however, must take a photo to obtain location information, raising confidentiality and privacy issues. To address these concerns, we propose a solution for passive visual positioning based on pedestrian detection and projection transformation. This method consists of three steps: pretreatment, pedestrian detection, and pose estimation. Pretreatment includes camera calibration and camera installation. In pedestrian detection, features are extracted by deep convolutional neural networks using neighboring frame detection results and the map information as the region of interest attention model (RIAM). Pose estimation computes accurate localization results through projection transformation (PT). This system relies on security cameras installed in non-private areas so that pedestrians do not have to take photos. Experiments were conducted in a hall about 100 square meters in size, with 41 test-points for the localization experiment. The results show that the positioning error was 0.48 m (RMSE) and the 90% error was 0.73 m. Therefore, the proposed passive visual method delivers high positioning performance.

A Time-Motion and Error Analysis of Speed Climbing in the 2019 IFSC Speed Climbing World Cup Final Rounds.

Speed climbing has become an Olympic event. However, there have been limited studies on the athletic performance of elite speed climbers under the current IFSC rule. Thus, this study aims to perform a statistical analysis of the performance of elite speed climbers and compare the different sex of the 2019 IFSC Speed Climbing World Cup. The 384 times climbing result in total climbing time, the time of four phases, and the start reaction time were calculated. In addition, the statistical data of men and women's total error rate in the final round, the error rate in each final round, as well as the body position and the phase when errors occurred were gathered. Several results were not found in previous studies. Firstly, there is no statistical significance between starting reaction and climbing time of male and female. Secondly, there was no significant correlation between phases of the route for male athletes. While there was a statistical correlation between adjacent stages for women, the time of women in each stage was significantly correlated with the previous stage (p < 0.05). The error rate of both men and women in the medal competition stage reached a high rate of ~50%. While the error rate of men in each phase of route has no significant difference, While the error rate of women in the fourth phase was significantly different from the first three parts (p < 0.05), gender-specific training procedures should be developed for elite athletes. Future research should test the psychological state and pressure of speed athletes in the competition.

TSA-SCC: Text Semantic-Aware Screen Content Coding With Ultra Low Bitrate.

Due to the rapid growth of web conferences, remote screen sharing, and online games, screen content has become an important type of internet media information and over 90% of online media interactions are screen based. Meanwhile, as the main component in the screen content, textual information averagely takes up over 40% of the whole image on various commonly used screen content datasets. However, it is difficult to compress the textual information by using the traditional coding schemes as HEVC, which assumes strong spatial and temporal correlations within the image/video. State-of-the-art screen content coding (SCC) standard as HEVC-SCC still adopts a block-based coding framework and does not consider the text semantics for compression, thus inevitably blurring texts at a lower bitrate. In this paper, we propose a general text semantic-aware screen content coding scheme (TSA-SCC) for ultra low bitrate setting. This method detects the abrupt picture in a screen content video (or image), recognizes textual information (including word, position, font type, font size and font color) in the abrupt picture based on neural networks, and encodes texts with text coding tools. The other pictures as well as the background image after removing texts from the abrupt picture via inpainting, are encoded with HEVC-SCC. Compared with HEVC-SCC, the proposed method TSA-SCC reduces bitrate by up to 3× at a similar compression quality. Moreover, TSA-SCC achieves much better visual quality with less bitrate consumption when encoding the screen content video/image at ultra low bitrates.

Comparison of hysterectomy and uterine artery embolization in the treatment of symptomatic uterine fibroids: A protocol for systematic review and meta-analysis.

BACKGROUND: Uterine fibroids are common benign tumors in premenopausal women. Surgery is the preferred treatment for symptomatic uterine fibroids. An alternative of hysterectomy to manage symptomatic uterine fibroids is selective uterine artery embolization. We performed a protocol for systematic review and meta-analysis to assess the effectiveness of uterine artery embolization for treating symptomatic uterine fibroids compared with hysterectomy. METHODS: The current systematic review and meta-analysis will be reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocol. This review protocol has been registered in the International Prospective Register of systematic reviews. Its registration number is CRD42022371866. Only randomized controlled trials (RCTs) are included in our study.Two independent reviewers will search for databases including PubMed, Embase, Cochrane Library website, ClinicalTrials.gov databases, Chinese National Knowledge Infrastructure Database, Wanfang database, and VIP database. The risk of bias in each included study will be assessed utilizing the Cochrane Collaboration's risk of bias tool. The RevMan 5.3 software (Cochrane Collaboration, Oxford, UK) will be used to conduct the meta-analyses. RESULTS: The results of this systematic review will be published in a peer-reviewed journal. CONCLUSION: This systematic review will provide high quality evidence to judge whether uterine artery embolization is an effective surgical method for patients with symptomatic uterine fibroids.

Joint Hand-Object 3D Reconstruction From a Single Image With Cross-Branch Feature Fusion.

Accurate 3D reconstruction of the hand and object shape from a hand-object image is important for understanding human-object interaction as well as human daily activities. Different from bare hand pose estimation, hand-object interaction poses a strong constraint on both the hand and its manipulated object, which suggests that hand configuration may be crucial contextual information for the object, and vice versa. However, current approaches address this task by training a two-branch network to reconstruct the hand and object separately with little communication between the two branches. In this work, we propose to consider hand and object jointly in feature space and explore the reciprocity of the two branches. We extensively investigate cross-branch feature fusion architectures with MLP or LSTM units. Among the investigated architectures, a variant with LSTM units that enhances object feature with hand feature shows the best performance gain. Moreover, we employ an auxiliary depth estimation module to augment the input RGB image with the estimated depth map, which further improves the reconstruction accuracy. Experiments conducted on public datasets demonstrate that our approach significantly outperforms existing approaches in terms of the reconstruction accuracy of objects.

Analyzing and Accelerating the Bottlenecks of Training Deep SNNs With Backpropagation.

Spiking neural networks (SNNs) with the event-driven manner of transmitting spikes consume ultra-low power on neuromorphic chips. However, training deep SNNs is still challenging compared to convolutional neural networks (CNNs). The SNN training algorithms have not achieved the same performance as CNNs. In this letter, we aim to understand the intrinsic limitations of SNN training to design better algorithms. First, the pros and cons of typical SNN training algorithms are analyzed. Then it is found that the spatiotemporal backpropagation algorithm (STBP) has potential in training deep SNNs due to its simplicity and fast convergence. Later, the main bottlenecks of the STBP algorithm are analyzed, and three conditions for training deep SNNs with the STBP algorithm are derived. By analyzing the connection between CNNs and SNNs, we propose a weight initialization algorithm to satisfy the three conditions. Moreover, we propose an error minimization method and a modified loss function to further improve the training performance. Experimental results show that the proposed method achieves 91.53% accuracy on the CIFAR10 data set with 1% accuracy increase over the STBP algorithm and decreases the training epochs on the MNIST data set to 15 epochs (over 13 times speed-up compared to the STBP algorithm). The proposed method also decreases classification latency by over 25 times compared to the CNN-SNN conversion algorithms. In addition, the proposed method works robustly for very deep SNNs, while the STBP algorithm fails in a 19-layer SNN.

Design of a Smartphone Indoor Positioning Dynamic Ground Truth Reference System Using Robust Visual Encoded Targets.

Smartphone indoor positioning ground truth is difficult to directly, dynamically, and precisely measure in real-time. To solve this problem, this paper proposes and implements a robust smartphone high-precision indoor positioning dynamic real-time ground truth reference system using color visual scatter-encoded targets based on machine vision and photogrammetry. First, a kind of novel high-precision color vision scatter-encoded patterns with a robust recognition rate is designed. Then we use a smartphone to obtain a sequence of images of an experimental room and extract the base points of the color visual scatter-encoded patterns from the sequence images to establish the indoor local coordinate system of the encoded targets. Finally, we use a high-efficiency algorithm to decode the targets of a real-time dynamic shooting image to obtain accurate instantaneous pose information of a smartphone camera and establish the high-precision and high-availability smartphone indoor positioning direct ground truth reference system for preliminary real-time accuracy evaluation of other smartphone positioning technologies. The experimental results show that the encoded targets of the color visual scatter-encoded pattern designed in this paper are easy to detect and identify, and the layout is simple and affordable. It can accurately and quickly solve the dynamic instantaneous pose of a smartphone camera to complete the self-positioning of the smartphone according to the artificial scatter feature visual positioning technology. It is a fast, efficient and low-cost accuracy-evaluation method for smartphone indoor positioning.

Wireless Fingerprinting Uncertainty Prediction Based on Machine Learning.

Although wireless fingerprinting has been well researched and widely used for indoor localization, its performance is difficult to quantify. Therefore, when wireless fingerprinting solutions are used as location updates in multi-sensor integration, it is challenging to set their weight accurately. To alleviate this issue, this paper focuses on predicting wireless fingerprinting location uncertainty by given received signal strength (RSS) measurements through the use of machine learning (ML). Two ML methods are used, including an artificial neural network (ANN)-based approach and a Gaussian distribution (GD)-based method. The predicted location uncertainty is evaluated and further used to set the measurement noises in the dead-reckoning/wireless fingerprinting integrated localization extended Kalman filter (EKF). Indoor walking test results indicated the possibility of predicting the wireless fingerprinting uncertainty through ANN the effectiveness of setting measurement noises adaptively in the integrated localization EKF.

Extrinsic Calibration of 2D Laser Rangefinders Using an Existing Cuboid-Shaped Corridor as the Reference.

Laser rangefinders (LRFs) are widely used in autonomous systems for indoor positioning and mobile mapping through the simultaneous localization and mapping (SLAM) approach. The extrinsic parameters of multiple LRFs need to be determined, and they are one of the key factors impacting system performance. This study presents an extrinsic calibration method of multiple LRFs that requires neither extra calibration sensors nor special artificial reference landmarks. Instead, it uses a naturally existing cuboid-shaped corridor as the calibration reference, and it hence needs no additional cost. The present method takes advantage of two types of geometric constraints for the calibration, which can be found in a common cuboid-shaped corridor. First, the corresponding point cloud is scanned by the set of LRFs. Second, the lines that are scanned on the corridor surfaces are extracted from the point cloud. Then, the lines within the same surface and the lines within two adjacent surfaces satisfy the coplanarity constraint and the orthogonality constraint, respectively. As such, the calibration problem is converted into a nonlinear optimization problem with the constraints. Simulation experiments and experiments based on real data verified the feasibility and stability of the proposed method.

A Bayesian Density Model Based Radio Signal Fingerprinting Positioning Method for Enhanced Usability.

Indoor navigation and location-based services increasingly show promising marketing prospects. Indoor positioning based on Wi-Fi radio signal has been studied for more than a decade because Wi-Fi, a signal of opportunity without extra cost, is extensively deployed for internet connections. Bayesian fingerprinting positioning, a classical Wi-Fi-based indoor positioning method, consists of two phases: radio map learning and position inference. Thus far, the application of Bayesian fingerprinting positioning is limited due to its poor usability; radio map learning requires an adequate number of received signal strength indication (RSSI) observables at each reference point, long-term fieldwork, and high development and maintenance costs. In this paper, based on a statistical analysis of actual RSSI observables, a Weibull⁻Bayesian density model is proposed to represent the probability density of Wi-Fi RSSI observables. The Weibull model, which is parameterized with three parameters that can be calculated with fewer samples, can calculate the probability density with a higher accuracy than the traditional histogram method. Furthermore, the parameterized Weibull model can simplify the radio map by storing only three parameters that can restore the whole probability density, i.e., it is not necessary to store the probability distribution based on traditionally separated RSSI bins. Bayesian positioning inference is performed in the positioning phase using probability density rather than the traditional probability distribution of predefined RSSI bins. The proposed method was implemented on an Android smartphone, and the performance was evaluated in different indoor environments. Results revealed that the proposed method enhanced the usability of Wi-Fi Bayesian fingerprinting positioning by requiring fewer RSSI observables and improved the positioning accuracy by 19⁻32% in different building environments compared with the classic histogram-based method, even when more samples were used.

Initial Assessment of the LEO Based Navigation Signal Augmentation System from Luojia-1A Satellite.

A low Earth orbiter (LEO)-based navigation signal augmentation system is considered as a complementary of current global navigation satellite systems (GNSS), which can accelerate precise positioning convergence, strengthen the signal power, and improve signal quality. Wuhan University is dedicated to LEO-based navigation signal augmentation research and launched one scientific experimental satellite named Luojia-1A. The satellite is capable of broadcasting dual-frequency band ranging signals over China. The initial performance of the Luojia-1A satellite navigation augmentation system is assessed in this study. The ground tests indicate that the phase noise of the oscillator is sufficiently low to support the intended applications. The field ranging tests achieve 2.6 m and 0.013 m ranging precision for the pseudorange and carrier phase measurements, respectively. The in-orbit test shows that the internal precision of the ephemeris is approximate 0.1 m and the clock stability is 3 × 10-10. The pseudorange and carrier phase measurement noise evaluated from the geometry-free combination is about 3.3 m and 1.8 cm. Overall, the Luojia-1A navigation augmentation system is capable of providing useable LEO navigation augmentation signals with the empirical user equivalent ranging error (UERE) no worse than 3.6 m, which can be integrated with existing GNSS to improve the real-time navigation performance.

Smartphone Heading Correction Based on Gravity Assisted and Middle Time Simulated-Zero Velocity Update Method.

Electronic appliances and ferromagnetic materials can be easily found in any building in urban environment. A steady magnetic environment and a pure value of geomagnetic field for calculating the heading of the smartphone in case of pedestrian walking indoors is hard to obtain. Therefore, an independent inertial heading correction algorithm without involving magnetic field but only making full use of the embedded Micro-Electro-Mechanical System (MEMS) Inertial measurement unit (IMU) device in the smartphone is presented in this paper. Aiming at the strict navigation requirements of pedestrian smartphone positioning, the algorithm focused in this paper consists of Gravity Assisted (GA) and Middle Time Simulated-Zero Velocity Update (MTS-ZUPT) methods. With the help of GA method, the different using-mode of the smartphone can be judged based on the data from the gravity sensor of smartphone. Since there is no zero-velocity status for handheld smartphone, the MTS-ZUPT algorithm is proposed based on the idea of Zero Velocity Update (ZUPT) algorithm. A Kalman Filtering algorithm is used to restrain the heading divergence at the middle moment of two steps. The walking experimental results indicate that the MTS-ZUPT algorithm can effectively restrain the heading error diffusion without the assistance of geomagnetic heading. When the MTS-ZUPT method was integrated with GA method, the smartphone navigation system can autonomously judge the using-mode and compensate the heading errors. The pedestrian positioning accuracy is significantly improved and the walking error is only 1.4% to 2.0% of the walking distance in using-mode experiments of the smartphone.

Improving GNSS Ambiguity Acceptance Test Performance with the Generalized Difference Test Approach.

In Global navigation satellite system (GNSS) data processing, integer ambiguity acceptance test is considered as a challenging problem. A number of ambiguity acceptance tests have been proposed from different perspective and then unified into the integer aperture estimation (IA) framework. Among all the IA estimators, the optimal integer aperture (OIA) achieves the highest success rate with the fixed failure rate tolerance. However, the OIA is of less practical appealing due to its high computation complexity. On the other hand, the popular discrimination tests employ only two integer candidates, which are the essential reason for their sub-optimality. In this study, a generalized difference test (GDT) is proposed to exploit the benefit of including three or more integer candidates to improve their performance from theoretical perspective. The simulation results indicate that the third best integer candidates contribute to more than 70% success rate improvement for integer bootstrapping success rate higher than 0.8 case. Therefore, the GDT with three integer candidates (GDT3) achieves a good trade-off between the performance and computation burden. The threshold function is also applied for rapid determination of the fixed failure rate (FF)-threshold for GDT3. The performance improvement of GDT3 is validated with real GNSS data set. The numerical results indicate that GDT3 achieves higher empirical success rate while the empirical failure rate remains comparable. In a 20 km baseline test, the success rate GDT3 increase 7% with almost the same empirical failure rate.

The Accuracy Comparison of Three Simultaneous Localization and Mapping (SLAM)-Based Indoor Mapping Technologies.

The growing interest and the market for indoor Location Based Service (LBS) have been drivers for a huge demand for building data and reconstructing and updating of indoor maps in recent years. The traditional static surveying and mapping methods can't meet the requirements for accuracy, efficiency and productivity in a complicated indoor environment. Utilizing a Simultaneous Localization and Mapping (SLAM)-based mapping system with ranging and/or camera sensors providing point cloud data for the maps is an auspicious alternative to solve such challenges. There are various kinds of implementations with different sensors, for instance LiDAR, depth cameras, event cameras, etc. Due to the different budgets, the hardware investments and the accuracy requirements of indoor maps are diverse. However, limited studies on evaluation of these mapping systems are available to offer a guideline of appropriate hardware selection. In this paper we try to characterize them and provide some extensive references for SLAM or mapping system selection for different applications. Two different indoor scenes (a L shaped corridor and an open style library) were selected to review and compare three different mapping systems, namely: (1) a commercial Matterport system equipped with depth cameras; (2) SLAMMER: a high accuracy small footprint LiDAR with a fusion of hector-slam and graph-slam approaches; and (3) NAVIS: a low-cost large footprint LiDAR with Improved Maximum Likelihood Estimation (IMLE) algorithm developed by the Finnish Geospatial Research Institute (FGI). Firstly, an L shaped corridor (2nd floor of FGI) with approximately 80 m length was selected as the testing field for Matterport testing. Due to the lack of quantitative evaluation of Matterport indoor mapping performance, we attempted to characterize the pros and cons of the system by carrying out six field tests with different settings. The results showed that the mapping trajectory would influence the final mapping results and therefore, there was optimal Matterport configuration for better indoor mapping results. Secondly, a medium-size indoor environment (the FGI open library) was selected for evaluation of the mapping accuracy of these three indoor mapping technologies: SLAMMER, NAVIS and Matterport. Indoor referenced maps were collected with a small footprint Terrestrial Laser Scanner (TLS) and using spherical registration targets. The 2D indoor maps generated by these three mapping technologies were assessed by comparing them with the reference 2D map for accuracy evaluation; two feature selection methods were also utilized for the evaluation: interactive selection and minimum bounding rectangles (MBRs) selection. The mapping RMS errors of SLAMMER, NAVIS and Matterport were 2.0 cm, 3.9 cm and 4.4 cm, respectively, for the interactively selected features, and the corresponding values using MBR features were 1.7 cm, 3.2 cm and 4.7 cm. The corresponding detection rates for the feature points were 100%, 98.9%, 92.3% for the interactive selected features and 100%, 97.3% and 94.7% for the automated processing. The results indicated that the accuracy of all the evaluated systems could generate indoor map at centimeter-level, but also variation of the density and quality of collected point clouds determined the applicability of a system into a specific LBS.

Indoor Visual Positioning Aided by CNN-Based Image Retrieval: Training-Free, 3D Modeling-Free.

Indoor localization is one of the fundamentals of location-based services (LBS) such as seamless indoor and outdoor navigation, location-based precision marketing, spatial cognition of robotics, etc. Visual features take up a dominant part of the information that helps human and robotics understand the environment, and many visual localization systems have been proposed. However, the problem of indoor visual localization has not been well settled due to the tough trade-off of accuracy and cost. To better address this problem, a localization method based on image retrieval is proposed in this paper, which mainly consists of two parts. The first one is CNN-based image retrieval phase, CNN features extracted by pre-trained deep convolutional neural networks (DCNNs) from images are utilized to compare the similarity, and the output of this part are the matched images of the target image. The second one is pose estimation phase that computes accurate localization result. Owing to the robust CNN feature extractor, our scheme is applicable to complex indoor environments and easily transplanted to outdoor environments. The pose estimation scheme was inspired by monocular visual odometer, therefore, only RGB images and poses of reference images are needed for accurate image geo-localization. Furthermore, our method attempts to use lightweight datum to present the scene. To evaluate the performance, experiments are conducted, and the result demonstrates that our scheme can efficiently result in high location accuracy as well as orientation estimation. Currently the positioning accuracy and usability enhanced compared with similar solutions. Furthermore, our idea has a good application foreground, because the algorithms of data acquisition and pose estimation are compatible with the current state of data expansion.

An Indoor Positioning System Based on Static Objects in Large Indoor Scenes by Using Smartphone Cameras.

The demand for location-based services (LBS) in large indoor spaces, such as airports, shopping malls, museums and libraries, has been increasing in recent years. However, there is still no fully applicable solution for indoor positioning and navigation like Global Navigation Satellite System (GNSS) solutions in outdoor environments. Positioning in indoor scenes by using smartphone cameras has its own advantages: no additional needed infrastructure, low cost and a large potential market due to the popularity of smartphones, etc. However, existing methods or systems based on smartphone cameras and visual algorithms have their own limitations when implemented in relatively large indoor spaces. To deal with this problem, we designed an indoor positioning system to locate users in large indoor scenes. The system uses common static objects as references, e.g., doors and windows, to locate users. By using smartphone cameras, our proposed system is able to detect static objects in large indoor spaces and then calculate the smartphones' position to locate users. The system integrates algorithms of deep learning and computer vision. Its cost is low because it does not require additional infrastructure. Experiments in an art museum with a complicated visual environment suggest that this method is able to achieve positioning accuracy within 1 m.