Outdoor Scenarios Research Articles

High-Performance Radiative Cooling Sunscreen.

Radiative cooling is a zero-energy-consumption cooling technology that shows great potential for outdoor human thermal management. To keep human skin comfortable in hot days, we herein develop a radiative cooling (RC) sunscreen that exhibits a low ultraviolet (UV) transmissivity (4.86%), a high solar reflectivity (90.19%), and a high mid-infrared emissivity (92.09%) to effectively provide both UV protection and skin cooling. As a result, the RC sunscreen exhibits a high cooling performance for decreasing the human skin temperature by 2.3-6.1 °C more than commercial sunscreens and 4.2-6.0 °C more than bare skin in a variety of outdoor scenarios in summer (e.g., low-humidity sunny days, high-humidity sunny days, and high-humidity cloudy days). In addition, the RC sunscreen also shows a good UV stability (12 h, 125 W), a high water resistance (106°), a long working life (30 days), and a good biocompatibility, thereby exhibiting promising commercial potentials in the sunscreen market.

Channel reconstruction through improvised deep learning architecture for high-speed networks

Efficient acquisition of channel state information (CSI) is quite complicated process but immensely essential to exploit probable benefits of massive multiple input multiple output (MIMO) systems. Therefore, a deep learningbased model is utilized to estimate channel feedback in a massive MIMO system. The proposed improvised deep learning-based channel estimation (IDLCE) model enhances channel reconstruction efficiency by using multiple convolutional layers and residual blocks. The proposed IDLCE model utilizes encoder network to compress CSI matrices where decoder network is used to downlink reconstruct CSI matrices. Here, an additional quantization block is incorporated to improve feedback reconstruction accuracy by reducing channel errors. A COST 2,100 model is adopted to analyse performance efficiency for both indoor and outdoor scenarios. Further, deep learning-based model is used to train thousands of parameter and correlation coefficients much faster and to minimize computational complexity. The proposed IDLCE model evaluate performance in terms of normalized mean square error (NMSE), correlation efficiency and reconstruction accuracy and compared against varied state-of-art-channel estimation techniques. Excellent performance results are obtained with large improvement in channel reconstruction accuracy

A Base-Map-Guided Global Localization Solution for Heterogeneous Robots Using a Co-View Context Descriptor

With the continuous advancement of autonomous driving technology, an increasing number of high-definition (HD) maps have been generated and stored in geospatial databases. These HD maps can provide strong localization support for mobile robots equipped with light detection and ranging (LiDAR) sensors. However, the global localization of heterogeneous robots under complex environments remains challenging. Most of the existing point cloud global localization methods perform poorly due to the different perspective views of heterogeneous robots. Leveraging existing HD maps, this paper proposes a base-map-guided heterogeneous robots localization solution. A novel co-view context descriptor with rotational invariance is developed to represent the characteristics of heterogeneous point clouds in a unified manner. The pre-set base map is divided into virtual scans, each of which generates a candidate co-view context descriptor. These descriptors are assigned to robots before operations. By matching the query co-view context descriptors of a working robot with the assigned candidate descriptors, the coarse localization is achieved. Finally, the refined localization is done through point cloud registration. The proposed solution can be applied to both single-robot and multi-robot global localization scenarios, especially when communication is impaired. The heterogeneous datasets used for the experiments cover both indoor and outdoor scenarios, utilizing various scanning modes. The average rotation and translation errors are within 1° and 0.30 m, indicating the proposed solution can provide reliable localization support despite communication failures, even across heterogeneous robots.

Enhanced outdoor visual localization using Py-Net voting segmentation approach.

Camera relocalization determines the position and orientation of a camera in a 3D space. Althouh methods based on scene coordinate regression yield highly accurate results in indoor scenes, they exhibit poor performance in outdoor scenarios due to their large scale and increased complexity. A visual localization method, Py-Net, is therefore proposed herein. Py-Net is based on voting segmentation and comprises a main encoder containing Py-layer and two branch decoders. The Py-layer comprises pyramid convolution and 1 × 1 convolution kernels for feature extraction across multiple levels, with fewer parameters to enhance the model's ability to extract scene information. Coordinate attention was added at the end of the encoder for feature correction, which improved the model robustness to interference. To prevent the feature loss caused by repetitive structures and low-texture images in the scene, deep over-parameterized convolution modules were incorporated into the seg and vote decoders. Landmark segmentation and voting maps were used to establish the relation between images and landmarks in 3D space, reducing anomalies and achieving high precision with a small number of landmarks. The experimental results show that, in multiple outdoor scenes, Py-Net achieves lower distance and angle errors compared to existing methods. Additionally, compared to VS-Net, which also uses a voting segmentation structure, Py-Net reduces the number of parameters by 31.85% and decreases the model size from 236MB to 170 MB.

Fast Motion State Estimation Based on Point Cloud by Combing Deep Learning and Spatio-Temporal Constraints

Moving objects in the environment have a higher priority and more challenges in growing domains like unmanned vehicles and intelligent robotics. Estimating the motion state of objects based on point clouds in outdoor scenarios is currently a challenging area of research. This is due to factors such as limited temporal information, large volumes of data, extended network processing times, and the ego-motion. The number of points in a point cloud frame is typically 60,000–120,000 points, but most current motion state estimation methods for point clouds only downsample to a few thousand points for fast processing. The downsampling step will lead to the loss of scene information, which means these methods are far from being used in practical applications. Thus, this paper proposes a motion state estimation method that combines spatio-temporal constraints and deep learning. It starts by estimating and compensating the ego-motion of multi-frame point cloud data and mapping multi-frame data to a unified coordinate system; then the point cloud motion segmentation model on the multi-frame point cloud is proposed for motion object segmentation. Finally, spatio-temporal constraints are utilized to correlate the moving object at different moments and estimate the motion vectors. Experiments on KITTI, nuScenes, and real captured data show that the proposed method has good results, with an average vector deviation of only 0.036 m and 0.043 m in KITTI and nuScenes under a processing time of about 80 ms. The EPE3D error under the KITTI data is only 0.076 m, which proves the effectiveness of the method.

Shadow fading analysis over asymmetric beam channel at 2.1 GHz in outdoor scenarios

AbstractIn this letter, we investigate the radio propagation characteristics in an open outdoor place through a frequency‐domain channel measurement campaign at a frequency band of 2.1 GHz via asymmetric beam patterns. Specifically, we focus on the large‐scale fading characteristics and analyze the transceivers' separation‐dependent and boresight direction‐dependent received power, shadow fading (SF), and SF auto‐correlation properties under the case of perfect beam alignment. Furthermore, the effects of asymmetric beamwidths are also thorough investigated. The measured results reveal that transceivers' beams can reduce the shadow fading variances evidently in contrast to the omnidirectional antennas' case. Besides, it is found that the pattern where mobile receiver (Rx) uses narrow beams and static transmitter (Tx) adopts the omnidirectional antenna yields a high SF auto‐correlation value compared to the other beam patterns. The conclusions help to understand the impacts of variable beamwidths on radio propagation.

SCANet: A lightweight deep learning network for massive MIMO CSI feedback based on spatial and channel attention mechanism

Multi-user massive multiple-input multiple-output (MIMO) communication systems consume too much downlink bandwidth due to the huge channel state information (CSI) feedback, deep learning-based CSI feedback approaches fortunately can alleviate the feedback overhead while obtaining an accurate CSI. However, there is a trade-off between the high feedback performance and low computational complexity. In this paper, a low-complexity CSI feedback approach is proposed based on spatial and channel attention mechanism, namely the Spatial and Channel Attention Network (SCANet). Specifically, the spatial and channel attention mechanism makes the network’s attention mainly focus on the specific spatial regions and key feature channels. We devise a serial architecture in the encoder that composes of Spatial and Channel Attention Block (SCAB) and Encoder Transformer Block. Moreover, we design a hybrid architecture in the decoder that composes of the CNNs Block and Decoder Transformer Block. These designs enable the network to effectively extract both global and local CSI features. Computer simulations in both the indoor and outdoor scenarios show that under the same system configurations, the proposed low-complexity SCANet achieves almost the same performance as the state-of-the-art network while reducing the computational complexity by 85.76% fewer floating-point operations per second (FLOPS) on average.

Salp Swarm Algorithm-Based Kalman Filter for Seamless Multi-Source Fusion Positioning with Global Positioning System/Inertial Navigation System/Smartphones

With the rapid development of high-precision positioning service applications, there is a growing demand for accurate and seamless positioning services in indoor and outdoor (I/O) scenarios. To address the problem of low localization accuracy in the I/O transition area and the difficulty of achieving fast and accurate I/O switching, a Kalman filter based on the salp swarm algorithm (SSA) for seamless multi-source fusion positioning of global positioning system/inertial navigation system/smartphones (GPS/INS/smartphones) is proposed. First, an Android smartphone was used to collect sensor measurement data, such as light, magnetometer, and satellite signal-to-noise ratios in different environments; then, the change rules of the data were analyzed, and an I/O detection algorithm based on the SSA was used to identify the locations of users. Second, the proposed I/O detection service was used as an automatic switching mechanism, and a seamless indoor–outdoor localization scheme based on improved Kalman filtering with K-L divergence is proposed. The experimental results showed that the SSA-based I/O switching model was able to accurately recognize environmental differences, and the average accuracy of judgment reached 97.04%. The localization method achieved accurate and continuous seamless navigation and improved the average localization accuracy by 53.79% compared with a traditional GPS/INS system.

DGD-CNet: Denoising Gated Recurrent Unit with a Dropout-Based CSI Network for IRS-Aided Massive MIMO Systems.

For the deployment of Sixth Generation (6G) networks, integrating Massive Multiple-Input Multiple-Output (Massive MIMO) systems with Intelligent Reflecting Surfaces (IRS) is highly recommended due to its significant benefits in reducing communication losses for Non-Line-of-Sight (NLoS) conditions. However, the use of passive IRS presents challenges in channel estimation, mainly due to the significant feedback overhead required in Frequency Division Duplex (FDD)-based Massive MIMO systems. To address these challenges, this paper introduces a novel Denoising Gated Recurrent Unit with a Dropout-based Channel state information Network (DGD-CNet). The proposed DGD-CNet model is specifically designed for FDD-based IRS-aided Massive MIMO systems, aiming to reduce the feedback overhead while improving the channel estimation accuracy. By leveraging the Dropout (DO) technique with the Gated Recurrent Unit (GRU), the DGD-CNet model enhances the channel estimation accuracy and effectively captures both spatial structures and time correlation in time-varying channels. The results show that the proposed DGD-CNet model outperformed existing models in the literature, achieving at least a 26% improvement in Normalized Mean Square Error (NMSE), a 2% increase in correlation coefficient, and a 4% in system accuracy under Low-Compression Ratio (Low-CR) in indoor situations. Additionally, the proposed model demonstrates effectiveness across different CRs and in outdoor scenarios.

Broad feature extraction and multi-directional imbalanced weighted broad learning system for the unsupervised stereo matching method

The practical application of supervised stereo matching methods is often limited by the requirement of obtaining ground-truth disparity maps in advance. To address this issue, we introduce a novel unsupervised stereo matching method based on the broad feature extraction and multi-directional imbalanced weighted broad learning system. In this study, an end-to-end unsupervised stereo matching method based on broad feature extraction was developed to generate an initial disparity map. Subsequently, a multi-directional imbalanced weighted broad learning system that considers the uneven distribution of disparity values was designed to effectively remove and redefine outlier disparities and enhance the accuracy of generating multiple candidate disparity maps. Multi-direction consistency verification was integrated into the system to further eliminate anomalous disparity values. This verification relies on uniqueness, which guarantees the consistency of the disparity truth values at the same location in various directions. Finally, the local gravity weight method based on a broad feature space was introduced to select a suitable disparity value from neighboring pixels to replace invalid pixel positions. Experiments were conducted under both indoor and outdoor scenarios to demonstrate the effectiveness and versatility of our approach, confirming that it outperforms state-of-the-art techniques, highlighting its superiority.

A novel pathloss prediction and optimization approach using deep learning in millimeter wave communication systems

Millimeter wave communication systems design requires accurate pathloss prediction as well as optimization. Traditional pathloss models gives less accuracy therefore pathloss models using deep learning is alternative solution for traditional method. These methods overcome the time consuming pathloss prediction parameter prediction based on large dataset which is available for different frequencies. In this proposed work, for prediction of pathloss, Convolutional Neural Network (CNN) and for optimization of pathloss Particle Swarm Optimization (PSO) is proposed. Towards implementation of the same, outdoor scenario with two roads and one cross section is used and dataset is generated using DeepMIMO generic dataset. The simulation result shows that using optimization technique reduces pathloss and CNN give accurate prediction of 95.7 %. Furthermore, root mean square error value of 6.6 dB has been achieve which is less in comparison to alternative approaches designed for outdoor environments. The forthcoming development of multifrequency path loss models has sparked significant interest due to their forecasting ability to support higher frequency bands and address blockage scenarios within future wireless networks.

A randomized controlled experiment testing the use of virtual reality to trigger cigarette craving in people who smoke

Automated delivery of therapy in virtual reality (VR) has the potential to be used for smoking cessation. Most obviously, it could be used to practise and establish alternative reactions to smoking cues. The first step in treatment development is to show that VR environments can trigger sufficient cravings in smokers. We evaluated a new VR public house outdoor scenario with 100 individuals who smoked daily. Participants were randomly assigned to the VR scenario with smoking cues or a neutral experience in VR. The VR experiences were presented in a standalone VR headset. Before and after VR, we collected self-reported craving scores for cigarettes and alcohol using the Tobacco Craving Questionnaire (TCQ) and visual analogue scales (VAS). Physiological data were also collected. Compared to the neutral condition, exposure to the smoking cues led to a large increase in craving for a cigarette (TCQ β = 11.44, p < 0.0001, Cohen’s d = 1.10) and also a moderate increase in craving for alcohol (β=0.7,p=0.017,d=0.50)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(\\upbeta =0.7,\ ext{ p}=0.017,\ ext{ d}=0.50)$$\\end{document}. There were no significant physiological differences between the two conditions. These results provide good evidence that VR experiences can elicit strong craving for cigarettes. The programming can be part of developing a new VR cognitive therapy to help people reduce smoking.

A Quality of Experience and Visual Attention Evaluation for 360° Videos with Non-spatial and Spatial Audio

This article presents the results of an empirical study that aimed to investigate the influence of various types of audio (spatial and non-spatial) on the user quality of experience (QoE) of and visual attention in 360° videos. The study compared the head pose, eye gaze, pupil dilations, heart rate, and subjective responses of 73 users who watched ten 360° videos with different sound configurations. The configurations evaluated were no sound; non-spatial (stereo) audio; and two spatial sound conditions (first- and third-order ambisonics). The videos covered various categories and presented both indoor and outdoor scenarios. The subjective responses were analyzed using an ANOVA (Analysis of Variance) to assess mean differences between sound conditions. Data visualization was also employed to enhance the interpretability of the results. The findings reveal diverse viewing patterns, physiological responses, and subjective experiences among users watching 360° videos with different sound conditions. Spatial audio, in particular third-order ambisonics, garnered heightened attention. This is evident in increased pupil dilation and heart rate. Furthermore, the presence of spatial audio led to more diverse head poses when sound sources were distributed across the scene. These findings have important implications for the development of effective techniques for optimizing processing, encoding, distributing, and rendering content in virtual reality (VR) and 360° videos with spatialized audio. These insights are also relevant in the creative realms of content design and enhancement. They provide valuable guidance on how spatial audio influences user attention, physiological responses, and overall subjective experiences. Understanding these dynamics can assist content creators and designers in crafting immersive experiences that leverage spatialized audio to captivate users, enhance engagement, and optimize the overall quality of VR and 360° video content. The dataset, scripts used for data collection, ffmpeg commands used for processing the videos, and the subjective questionnaire and its statistical analysis are publicly available.

Lidar pose estimation method based on factor graph optimization

Abstract In the realm of computer vision and robotics, lidar SLAM is an important technical means. Lidar SLAM frequently serves to ascertain the spatial coordinates and orientation of mobile robots amidst their surroundings. In this paper, an adaptive factor graph optimization of the lidar pose estimation method using only point-to-line constraints is proposed. The algorithm only employs the measure of distance between a point and a line to construct the constraint problem and solves the pose through point-to-line ICP and the pose with higher accuracy by optimizing the factor graph. The robustness of mobile robots in indoor scenes is improved. According to different application scenarios, including street and gate, comparative experiments are carried out on the open-source data set M2DGR containing lidar data. Empirical findings demonstrate that the algorithm put forth enhances the precision of the lidar odometer when confronted with outdoor scenarios, thereby bolstering the resilience of mobile robots traversing urban thoroughfares.

Path loss, angular spread and channel sparsity modeling for indoor and outdoor environments at the sub-THz band

In this paper, we present new measurement results to model large-scale path loss, angular spread and channel sparsity at the sub-THz (141–145 GHz) band, for both indoor and outdoor scenarios. Extensive measurement campaigns have been carried out, taking into account both line-of-sight (LoS) and non line-of-sight (NLoS) propagation. For all considered propagation scenarios, omni-directional and directional path loss models have been developed, based on the so-called close-in (CI) free-space reference distance model. A power angular spread analysis is further presented. The sparsity of the wireless channel has also been studied by employing suitable metrics, namely the so-called Gini index (GI), the Ricean K-factor and the root mean square (RMS) delay spread.

BlockFusion: Expandable 3D Scene Generation using Latent Tri-plane Extrapolation

We present BlockFusion, a diffusion-based model that generates 3D scenes as unit blocks and seamlessly incorporates new blocks to extend the scene. BlockFusion is trained using datasets of 3D blocks that are randomly cropped from complete 3D scene meshes. Through per-block fitting, all training blocks are converted into the hybrid neural fields: with a tri-plane containing the geometry features, followed by a Multi-layer Perceptron (MLP) for decoding the signed distance values. A variational auto-encoder is employed to compress the tri-planes into the latent tri-plane space, on which the denoising diffusion process is performed. Diffusion applied to the latent representations allows for high-quality and diverse 3D scene generation. To expand a scene during generation, one needs only to append empty blocks to overlap with the current scene and extrapolate existing latent tri-planes to populate new blocks. The extrapolation is done by conditioning the generation process with the feature samples from the overlapping tri-planes during the denoising iterations. Latent tri-plane extrapolation produces semantically and geometrically meaningful transitions that harmoniously blend with the existing scene. A 2D layout conditioning mechanism is used to control the placement and arrangement of scene elements. Experimental results indicate that BlockFusion is capable of generating diverse, geometrically consistent and unbounded large 3D scenes with unprecedented high-quality shapes in both indoor and outdoor scenarios.

Sunlight optimization in residential area design: Introducing sOSA - A comprehensive indicator for swift assessment of outdoor sunshine exposure

The increasing density and height of residential buildings have heightened the concerns regarding inadequate sunshine exposure in outdoor areas. With multi-objective optimization experiments, existing evaluation measures, such as Sunshine Times (ST), are not able to sufficiently capture the spatial features required to find the best design for outdoor sunlight conditions quickly. Spatial Outdoor Sunshine Autonomy (sOSA) is proposed in this paper as a comprehensive indicator for the rapid assessment of cumulative sunlight exposure in various outdoor scenarios. The definition of the indicator is first presented through formula derivation. Subsequently, the rapid simulation method of the sOSA indicator in Rhino is demonstrated. Utilizing the Maigao Bridge Block in Nanjing as a prototype, the application of this indicator in multi-objective optimization experiments for new residential area planning and design is showcased. Finally, the results are evaluated, and the suggestion is made to combine sOSA with ST maps and wind speed cloud maps for enhanced practical guidance.

Unsupervised 3D Object Segmentation of Point Clouds by Geometry Consistency.

In this paper, we study the problem of 3D object segmentation from raw point clouds. Unlike existing methods which usually require a large amount of human annotations for full supervision, we propose the first unsupervised method, called OGC, to simultaneously identify multiple 3D objects in a single forward pass, without needing any type of human annotations. The key to our approach is to fully leverage the dynamic motion patterns over sequential point clouds as supervision signals to automatically discover rigid objects. Our method consists of three major components, 1) the object segmentation network to directly estimate multi-object masks from a single point cloud frame,2)the auxiliary self-supervised scene flow estimator,and 3)our core object geometry consistency component. By carefully designing a series of loss functions, we effectively take into account the multi-object rigid consistency and the object shape invariance in both temporal and spatial scales. This allows our method to truly discover the object geometry even in the absence of annotations. We extensively evaluate our method on five datasets, demonstrating the superior performance for object part instance segmentation and general object segmentation in both indoor and the challenging outdoor scenarios. Our code and data are available at https://github.com/vLAR-group/OGC.

Modelling polarized pulsed LiDAR signals in scattering media: an approach for optimizing systems in real-world scenarios.

Adverse weather conditions present a primary challenge for ground-based LiDAR imaging systems in outdoor applications. The use of polarization has been proposed as an effective filtering mechanism. However, the number of potential situations is large, complex and difficult to parameterize with accuracy. In such conditions, advanced simulation methods enable the testing of different experimental configurations and the determination of the best possible setup. With this purpose, a Monte Carlo algorithm is presented for modeling polarized pulsed LiDAR signals in turbid media. This algorithm is designed for immediate applicability, incorporating realistic media characterization and accounting for the attributes of existing prototypes. It allows testing various experimental configurations, managing optical obstacles, adjusting polarization arrangements, and different geometries of particles within the medium. The developed algorithm accurately characterizes backscattering signals, revealing their dependence on medium properties. A relationship between visibility and backscattering energy is identified, offering insights for sensor optimization. Polarization analysis highlights the efficacy of circular polarization in mitigating scattering effects and establishes a connection with the polarimetric characteristics of imaged targets. The algorithm's application to irregular particles reveals also an unexpected behavior of polarized light, challenging established strategies. These diverse use cases exemplify the algorithm's capability to model real-world circumstances, emphasizing its significance in predicting cutting-edge situations when designing optical systems for complex and demanding outdoor scenarios.

Design &amp; Fabrication of Vertical Surface Cleaner

Abstract: The Wall Climbing Robot designed in this study to combines the innovative propulsion system of propellers with the traditional locomotion method of wheels to achieve versatile and efficient mobility on both vertical and horizontal surfaces. By integrating propellers and wheels, the robot can seamlessly transition between climbing walls and moving along flat surfaces. The hybrid design not only enhances the robot's mobility but also optimizes its energy efficiency, making it suitable for prolonged missions in various environments. This research explores the synergistic integration of propellers and wheels, showcasing the robot's adaptability in real- world applications such as surveillance, inspection, and maintenance tasks in both indoor and outdoor scenarios. The developed Wall Climbing Robot presents a significant advancement in robotic mobility, offering a promising solution for complex tasks in diverse settings