Complex Indoor Environments Research Articles

Constrained MEMS-Based INS/UWB Tightly Coupled System for Accurate UGVs Navigation

To enhance the navigation performance and robustness of navigation system combining ultrawideband (UWB) and inertial navigation systems (INS) under complex indoor environments, an improved navigation method—Allan variance (AV) to assist a modified adaptive extended Kalman Filter based on the dynamic weight function (DWF-MAEFF)—is proposed. Firstly, AV is used to improved INS error dynamics by modeling the stochastic noise of an inertial sensor; which can compensate for inertial sensor error caused by stochastic noise during integrated navigation. Secondly, the MAEKF is developed by designing the weight function to adjust the weight of measurement noise reasonably and dynamically, which can further improve the robustness of the AEKF algorithm. Field tests were conducted to verify the effectiveness of the proposed navigation method. The result indicated that an improvement of up to 60% over the existing integrated navigation method based on EKF and AEKF can be obtained by the proposed method.

MARSIM: A Light-Weight Point-Realistic Simulator for LiDAR-Based UAVs

The emergence of low-cost, small form factor and light-weight solid-state LiDAR sensors have brought new opportunities for autonomous unmanned aerial vehicles (UAVs) by advancing navigation safety and computation efficiency. Yet the successful developments of LiDAR-based UAVs must rely on extensive simulations. Existing simulators can hardly perform simulations of real-world environments due to the requirements of dense mesh maps that are difficult to obtain. In this paper, we develop a point-realistic simulator of real-world scenes for LiDAR-based UAVs. The key idea is the underlying point rendering method, where we construct a depth image directly from the point cloud map and interpolate it to obtain realistic LiDAR point measurements. Our developed simulator is able to run on a light-weight computing platform and supports the simulation of LiDARs with different resolution and scanning patterns (e.g., spinning LiDARs and solid-state LiDARs), dynamic obstacles, and multi-UAV systems. Developed in the ROS framework, the simulator can easily communicate with other key modules of an autonomous robot, such as perception, state estimation, planning, and control. Finally, the simulator provides 10 high-resolution point cloud maps of various real-world environments, including forests of different densities, historic building, office, parking garage, and various complex indoor environments. These realistic maps provide diverse testing scenarios for an autonomous UAV. Evaluation results show that the developed simulator achieves superior performance in terms of time and memory consumption against Gazebo and that the simulated UAV flights highly match the actual one in real-world environments. We believe such a point-realistic and light-weight simulator is crucial to bridge the gap between UAV simulation and experiments and will significantly facilitate the research of LiDAR-based autonomous UAVs in the future.

A Robust Indoor Localization Method Based on DAT-SLAM and Template Matching Visual Odometry

Robust positioning is a central issue in robots. Due to the complex indoor environment, visual simultaneous localization and mapping (VSLAM) is susceptible to light and some scenes contain few features. The robot cannot obtain an accurate position. To solve these problems, this work first proposes dynamic adaptive threshold simultaneous localization and mapping (DAT-SLAM), a method for extracting feature points based on a dynamic adaptive threshold. The method is stable for feature extraction under illumination transformation. Subsequently, template matching visual odometry (VO) is introduced and combined with DAT-SLAM to form a joint indoor localization framework. Experiments on the datasets show that DAT-SLAM has a better performance than oriented fast and rotated brief SLAM2 (ORB-SLAM2). The mean positioning accuracy is improved by 11.74%. Experiments in real scenes show that the joint localization framework can achieve continuous localization in scenes with sparse features.

Scene Recognition for Device-Free Indoor Localization

Device-free indoor localization methods based on Channel State Information (CSI) have become an increasingly important topic. In complex indoor environments, both the line-of-sight (LOS) and non-LOS areas coexist, and the optimal parameters of the localization models for these two areas are different. In order to address this problem, in this paper, the scene-recognition indoor localization method is proposed to identify LOS and NLOS areas. First, the scene recognition model is given by combining mutation particle swarm optimization (MPSO) with backpropagation (BP) neural network. Then, the feature transformation method based on discriminant correlation analysis (DCA) is presented, which can effectively explore the correlation of amplitude data and phase data and form high-resolution location fingerprints, and further improve the indoor localization accuracy. Experimental results show that compared with the PAIL, LCAF, LSTM, BLS, PCNB, ABPS and FapFi algorithms, the proposed algorithm has higher localization accuracy.

Range-Extension Algorithms and Strategies for TDOA Ultra-Wideband Positioning System.

The Internet of Things (IoT) for smart industry requires the surveillance and management of people and objects. The ultra-wideband positioning system is an attractive solution for achieving centimeter-level accuracy in target location. While many studies have focused on improving the accuracy of the anchor coverage range, it is important to note that in practical applications, positioning areas are often limited and obstructed by furniture, shelves, pillars, or walls, which can restrict the placement of anchors. Furthermore, some positioning regions are located beyond anchor coverage, and a single group with few anchors may not be able to cover all rooms and aisles on a floor due to non-line-of-sight errors causing severe positioning errors. In this work, we propose a dynamic-reference anchor time difference of arrival (TDOA) compensation algorithm to enhance accuracy beyond anchor coverage by eliminating local minima of the TDOA loss function near anchors. We designed a multidimensional and multigroup TDOA positioning system with the aim of broadening the coverage of indoor positioning and accommodating complex indoor environments. By employing an address-filter technique and group-switching process, tags can seamlessly move between groups with a high positioning rate, low latency, and high accuracy. We deployed the system in a medical center to locate and manage researchers with infectious medical waste, demonstrating its usefulness for practical healthcare institutions. Our proposed positioning system can thus facilitate precise and wide-range indoor and outdoor wireless localization.

Rendering massive indoor point clouds in virtual reality

This paper addresses the challenges of rendering massive indoor point clouds in Virtual Reality. In these kind of visualizations the point of view is never static, imposing the need of a one-shot (i.e. non-iterative) rendering strategy, in contrast with progressive refinement approaches that assume that the camera position does not change between most consecutive frames. Our approach benefits from the static nature of indoor environments to pre-compute a visibility map that enables us to boost real-time rendering performance. The key idea behind our visibility map is to exploit the cluttered topology of buildings in order to effectively cull the regions of the space that are occluded by structural elements such as walls. This does not only improve performance but also the visual quality of the final render, allowing us to display in full detail the space and preventing the user to see the contiguous spaces through the walls. Additionally, we introduce a novel hierarchical data structure that enables us to display the point cloud with a continuous level of detail with a minimal impact on performance. Experimental results show that our approach outperforms state-of-the-art techniques in complex indoor environments and achieves comparable results in outdoor ones, proving the generality of our method.

Fingerprint-Based Localization Approach for WSN Using Machine Learning Models

The area of localization in wireless sensor networks (WSNs) has received considerable attention recently, driven by the need to develop an accurate localization system with the minimum cost and energy consumption possible. On the other hand, machine learning (ML) algorithms have been employed widely in several WSN-based applications (data gathering, clustering, energy-harvesting, and node localization) and showed an enhancement in the obtained results. In this paper, an efficient WSN-based fingerprinting localization system for indoor environments based on a low-cost sensor architecture, through establishing an indoor fingerprinting dataset and adopting four tailored ML models, is presented. The proposed system was validated by real experiments conducted in complex indoor environments with several obstacles and walls and achieves an efficient localization accuracy with an average of 1.4 m. In addition, through real experiments, we analyze and discuss the impact of reference point density on localization accuracy.

Channel State Identification in Complex Indoor Environments With ST-CNN and Transfer Learning

Accurate channel state identification can improve the accuracy of ultrawideband (UWB) indoor positioning. In complex indoor environments, existing channel state identification methods suffer from high costs. Meanwhile, these methods pay little attention to the different localization errors caused by weak -non-line-of-sight (WLOS) and non-line-of-sight (NLOS). In this letter, a channel state identification method that can identify line-of-sight (LOS), WLOS, NLOS is proposed for use in a single indoor scenario based on the Stockwell transform and convolutional neural networks (ST-CNN). Moreover, combined with transfer learning (TL), ST-CNN can reduce the cost of identification in complex indoor environments. Simulations based on open source datasets show that ST-CNN can achieves F2 scores of 97.17% and 97.14% in NLOS and WLOS. For complex indoor environments consisting of multiple indoor scenarios, compared with the ST-CNN, the ST-CNN+TL method can reduce the training time by 62% and the training data by 40%.

A robust indoor localization algorithm based on polynomial fitting and Gaussian mixed model

Wireless sensor network (WSN) positioning has a good effect on indoor positioning, so it has received extensive attention in the field of positioning. Non-line-of sight (NLOS) is a primary challenge in indoor complex environment. In this paper, a robust localization algorithm based on Gaussian mixture model and fitting polynomial is proposed to solve the problem of NLOS error. Firstly, fitting polynomials are used to predict the measured values. The residuals of predicted and measured values are clustered by Gaussian mixture model (GMM). The LOS probability and NLOS probability are calculated according to the clustering centers. The measured values are filtered by Kalman filter (KF), variable parameter unscented Kalman filter (VPUKF) and variable parameter particle filter (VPPF) in turn. The distance value processed by KF and VPUKF and the distance value processed by KF, VPUKF and VPPF are combined according to probability. Finally, the maximum likelihood method is used to calculate the position coordinate estimation. Through simulation comparison, the proposed algorithm has better positioning accuracy than several comparison algorithms in this paper. And it shows strong robustness in strong NLOS environment.

An Improved Probabilistic Roadmap Planning Method for Safe Indoor Flights of Unmanned Aerial Vehicles

Unmanned aerial vehicles (UAVs) have been widely used in industry and daily life, where safety is the primary consideration, resulting in their use in open outdoor environments, which are wider than complex indoor environments. However, the demand is growing for deploying UAVs indoors for specific tasks such as inspection, supervision, transportation, and management. To broaden indoor applications while ensuring safety, the quadrotor is notable for its motion flexibility, particularly in the vertical direction. In this study, we developed an improved probabilistic roadmap (PRM) planning method for safe indoor flights based on the assumption of a quadrotor model UAV. First, to represent and model a 3D environment, we generated a reduced-dimensional map using a point cloud projection method. Second, to deploy UAV indoor missions and ensure safety, we improved the PRM planning method and obtained a collision-free flight path for the UAV. Lastly, to optimize the overall mission, we performed postprocessing optimization on the path, avoiding redundant flights. We conducted experiments to validate the effectiveness and efficiency of the proposed method on both desktop and onboard PC, in terms of path-finding success rate, planning time, and path length. The results showed that our method ensures safe indoor UAV flights while significantly improving computational efficiency.

A UAV-based explore-then-exploit system for autonomous indoor facility inspection and scene reconstruction

Traditional indoor facility inspections on pipelines and boilers are conducted manually and can be logistically challenging, labor-intensive, costly, and dangerous for the inspectors. With the maturity of unmanned technology, the unmanned aerial vehicle (UAV) is becoming a promising alternative to the problematic manual inspection. However, due to the lack of GPS signal indoor, the localization of UAVs is a big challenge to achieve fully autonomous inspection. Moreover, the narrow and complex indoor environment makes it difficult to guarantee flight safety. This paper presents a UAV-based explore-then-exploit system to tackle these problems for autonomous indoor facility data collection and scene reconstruction. The proposed system consists of a hardware description and integration of two UAVs, a two-step simultaneous localization and mapping (SLAM) method for UAV localization and 3D environmental mapping, a safety-guaranteed coverage path planning algorithm for inspection and data collection, as well as an obstacle-aware trajectory generation method. The proposed system is examined in GPS-denied and cluttered indoor environment and 3D scene reconstruction is conducted. The quantitative analysis shows that the positioning accuracy is centimeter-level and the reconstruction error is within 3 cm. The performance analysis demonstrates the robustness and feasibility of our system in reconstructing and inspecting complex indoor environments for high-efficiency and low-cost facility management.

A SLAM-Based Localization and Navigation System for Social Robots: The Pepper Robot Case

Robot navigation in indoor environments has become an essential task for several applications, including situations in which a mobile robot needs to travel independently to a certain location safely and using the shortest path possible. However, indoor robot navigation faces challenges, such as obstacles and a dynamic environment. This paper addresses the problem of social robot navigation in dynamic indoor environments, through developing an efficient SLAM-based localization and navigation system for service robots using the Pepper robot platform. In addition, this paper discusses the issue of developing this system in a way that allows the robot to navigate freely in complex indoor environments and efficiently interact with humans. The developed Pepper-based navigation system has been validated using the Robot Operating System (ROS), an efficient robot platform architecture, in two different indoor environments. The obtained results show an efficient navigation system with an average localization error of 0.51 m and a user acceptability level of 86.1%.

Multi-Level Fusion Indoor Positioning Technology Considering Credible Evaluation Analysis

Aiming at the problems of the low robustness and poor reliability of a single positioning source in complex indoor environments, a multi-level fusion indoor positioning technology considering credible evaluation is proposed. A multi-dimensional electromagnetic atlas including pseudolites (PL), Wi-Fi and a geomagnetic field is constructed, and the unsupervised learning model is used to sample in the latent space to achieve a feature-level fusion positioning. A location credibility evaluation method is designed to improve the credibility of the positioning system through a multi-dimensional data quality evaluation and heterogeneous information auxiliary constraints. Finally, a large number of experiments were carried out in the laboratory environment, and, finally, about 90% of the positioning error was better than 1 m, and the average positioning error was 0.56 m. Compared with several relatively advanced positioning methods (Inter-satellite CPDM/Epoch-CPDS/Z-KPI) at present, the average positioning accuracy is improved by about 56%, 83.5% and 82.9%, respectively, which verifies the effectiveness of the algorithm. To verify the effect of the proposed method in a practical application environment, the proposed positioning system is deployed in the 2022 Winter Olympics venues. The results show that the proposed method has a significant improvement in the positioning accuracy and continuity.

Research on AGV positioning method combined with IMU and UWB

Aiming at the problem that automated guided vehicle (AGV) is difficult to locate accurately due to the influence of environment and time drift when it works in the indoor intelligent storage system. In this paper, an extended kalman filtering (EKF) framework is designed. In order to make full use of the original ranging values of ultra wideband (UWB) and inertial measurement unit (IMU), the framework realizes the fusion positioning between UWB module and IMU module in a tight coupling manner, so as to ensure that the system can still work when the available base station signal is inaccurate. Firstly, for the problem that the traditional UWB positioning method is easily affected by the non-line of sight (NLOS) error indoors, the calculated positioning coordinate value is unstable. With the help of different NLOS probability distribution curves of different obstacles, the weighted least square method is applied to the UWB positioning method to determine the positioning coordinate value of UWB, which improves the sudden change of AGV positioning coordinate in the static environment. Then the data fusion algorithm is optimized, and the error value of IMU and UWB coordinate is taken as the observation value of EKF, which reduces the influence of cumulative error on IMU positioning results, provides the global optimal estimation of the system optimal state, and improves the fusion positioning accuracy. Finally, the measured data of UWB and IMU systems in indoor complex environment are simulated in MATLAB. The experimental results show that when NLOS signal seriously affects the positioning effect, the UWB and IMU combined positioning system can provide more reliable positioning results than the single IMU positioning system. It improves the positioning accuracy of AGV and provides a new idea for indoor positioning mode.

Wireless methods for the local positioning without ACCESS to the Internet and GPS service

Local positioning in buildings without access to the Internet is a crucial requirement in many scenarios, such as large corporate offices, hospitals, universities, and shopping malls. However, this need for local positioning also presents certain challenges and problems that must be addressed. One of the most significant challenges of local positioning in buildings without internet access is the lack of reliable and up-to-date indoor map data. Without accurate map data, it can be difficult to navigate through complex indoor environments or locate specific rooms or facilities within a building. Another challenge is the absence of GPS signals in indoor environments, which makes it challenging to obtain accurate location information. This is especially problematic when attempting to track the movement of people or objects within a building.Additionally, the lack of internet access can make it difficult to implement location-based services that rely on data transfer between devices. This includes applications like indoor navigation, asset tracking, and location-based marketing, which all require internet connectivity to function correctly. Despite these challenges, there is a growing need for local positioning in buildings without internet access. This is especially true for large organizations and institutions that rely on efficient and effective operations within their indoor environments. Some technologies can provide accurate location information and help to mitigate some of the challenges associated with local positioning in buildings without internet access. They allows to obtain the accurate location information without relying on GPS signals or internet connectivity.The need for local positioning in buildings without internet access is becoming increasingly important, especially for large organizations and institutions that require efficient and effective operations within their indoor environments.

Indoor Spatiotemporal Contact Analytics Using Landmark-Aided Pedestrian Dead Reckoning on Smartphones.

Due to the prevalence of COVID-19, providing safe environments and reducing the risks of virus exposure play pivotal roles in our daily lives. Contact tracing is a well-established and widely-used approach to track and suppress the spread of viruses. Most digital contact tracing systems can detect direct face-to-face contact based on estimated proximity, without quantifying the exposed virus concentration. In particular, they rarely allow for quantitative analysis of indirect environmental exposure due to virus survival time in the air and constant airborne transmission. In this work, we propose an indoor spatiotemporal contact awareness framework (iSTCA), which explicitly considers the self-containing quantitative contact analytics approach with spatiotemporal information to provide accurate awareness of the virus quanta concentration in different origins at various times. Smartphone-based pedestrian dead reckoning (PDR) is employed to precisely detect the locations and trajectories for distance estimation and time assessment without the need to deploy extra infrastructure. The PDR technique we employ calibrates the accumulative error by identifying spatial landmarks automatically. We utilized a custom deep learning model composed of bidirectional long short-term memory (Bi-LSTM) and multi-head convolutional neural networks (CNNs) for extracting the local correlation and long-term dependency to recognize landmarks. By considering the spatial distance and time difference in an integrated manner, we can quantify the virus quanta concentration of the entire indoor environment at any time with all contributed virus particles. We conducted an extensive experiment based on practical scenarios to evaluate the performance of the proposed system, showing that the average positioning error is reduced to less than 0.7 m with high confidence and demonstrating the validity of our system for the virus quanta concentration quantification involving virus movement in a complex indoor environment.

Research on UWB Indoor Positioning Algorithm under the Influence of Human Occlusion and Spatial NLOS

Ultra-wideband (UWB) time-of-flight (TOF)-based ranging information in a non-line-of-sight (NLOS) environment can display significant forward errors, which directly affect positioning performance. NLOS has been a major factor limiting the improvement of UWB positioning accuracy and its application in complex scenarios. Therefore, in order to weaken the influence of the indoor complex environment on the NLOS environment of UWB and to further improve the performance of positioning, in this paper, we first analyze the factors and characteristics of NLOS formation in an indoor environment. The NLOS is divided into fixed NLOS influenced by spatial structure and dynamic random NLOS influenced by human occlusion. Then, the anchor LOS/NLOS information map is established by making full use of indoor spatial a priori information. On this basis, a robust adaptive extended Kalman filtering algorithm based on the anchor LOS/NLOS information map is designed, which is not only effectively able to exclude the influence of spatial NLOS, but can also optimize the random error. The proposed algorithm was validated in different experimental scenarios. The experimental results show that the positioning accuracy is better than 0.32 m in complex indoor NLOS environments.

YOLOv4‐tiny‐based robust RGB‐D SLAM approach with point and surface feature fusion in complex indoor environments

AbstractAlgorithm frameworks based on feature point matching are mature and widely used in simultaneous localization and mapping (SLAM). However, in the complex and changeable indoor environment, feature point matching‐based SLAM currently has two major problems, namely, decreased accuracy of pose estimation due to the interference caused by dynamic objects to the SLAM system and tracking loss caused by the lack of feature points in weak texture scenes. To address these problems, herein, we present a robust and real‐time RGB‐D SLAM algorithm that is based on ORBSLAM3. For interference caused by indoor moving objects, we add the improved lightweight object detection network YOLOv4‐tiny to detect dynamic regions, and the dynamic features in the dynamic area are then eliminated in the algorithm tracking stage. In the case of indoor weak texture scenes, while extracting point features the system extracts surface features at the same time. The framework fuses point and surface features to track camera pose. Experiments on the public TUM RGB‐D data sets show that compared with the ORB‐SLAM3 algorithm in highly dynamic scenes, the root mean square error (RMSE) of the absolute path error of the proposed algorithm improved by an average of 94.08%. Camera pose is tracked without loss over time. The algorithm takes an average of 34 ms to track each frame of the picture just with a CPU, which is suitably real‐time and practical. The proposed algorithm is compared with other similar algorithms, and it exhibits excellent real‐time performance and accuracy. We also used a Kinect camera to evaluate our algorithm in complex indoor environment, and also showed high robustness and real‐time. To sum up, our algorithm can not only deal with the interference caused by dynamic objects to the system but also stably run in the open indoor weak texture scene.

Adaptive estimation of UAV altitude in complex indoor environments using degraded and time-delayed measurements with time-varying uncertainties

A novel approach for robust Unmanned Aerial Vehicle (UAV) altitude estimation relying on laser measurements that is designed for use in complex indoor environments is proposed in this paper. Specifically, we aim to design a system with general usability inside multi-floor buildings. The multi-floor buildings are characterized by areas lacking distinct vertical geometric features to be used as reference by 3D Light Detection and Ranging (LiDAR) localization algorithms, and by areas with either flat floors or limited areas with inconsistent ground elevation. The proposed approach solves the problem of adaptive fusion of data from multiple sources with apriori-unknown confidence dependent on the current environmental properties. Whenever the environment contains enough geometric structure, altitude data from a 3D LiDAR-based Simultaneous Localization and Mapping (SLAM) algorithm are utilized. In environments that are too symmetrical for reliable SLAM operation, the approach relies mostly on measurements from a downward-facing 1D laser rangefinder, while simultaneously detecting inconsistent ground elevation areas. These measurements are fused with barometer data, Inertial Measurement Unit (IMU) data, and information from the UAV position controllers. Furthermore, our approach correctly handles the measurement delay caused by 3D LiDAR data processing that significantly differs from other sensor delays. The performance of the proposed approach has been validated in complex simulations and real-world experiments with the produced altitude estimate utilized in the control loop of the UAV. The proposed approach is released as open-source as part of the MRS UAV System.

Intelligent indoor metasurface robotics.

Intelligent indoor robotics is expected to rapidly gain importance in crucial areas of our modern society such as at-home health care and factories. Yet, existing mobile robots are limited in their ability to perceive and respond to dynamically evolving complex indoor environments because of their inherently limited sensing and computing resources that are, moreover, traded off against their cruise time and payload. To address these formidable challenges, here we propose intelligent indoor metasurface robotics (I2MR), where all sensing and computing are relegated to a centralized robotic brain endowed with microwave perception; and I2MR's limbs (motorized vehicles, airborne drones, etc.) merely execute the wirelessly received instructions from the brain. The key aspect of our concept is the centralized use of a computation-enabled programmable metasurface that can flexibly mold microwave propagation in the indoor wireless environment, including a sensing and localization modality based on configurational diversity and a communication modality to establish a preferential high-capacity wireless link between the I2MR's brain and limbs. The metasurface-enhanced microwave perception is capable of realizing low-latency and high-resolution three-dimensional imaging of humans, even around corners and behind thick concrete walls, which is the basis for action decisions of the I2MR's brain. I2MR is thus endowed with real-time and full-context awareness of its operating indoor environment. We implement, experimentally, a proof-of-principle demonstration at ∼2.4GHz, in which I2MR provides health-care assistance to a human inhabitant. The presented strategy opens a new avenue for the conception of smart and wirelessly networked indoor robotics.