Environment Map Construction Research Articles

Analysis of trust and efficient take-over strategy with multi-sensor information fusion in conditionally automated driving

Abstract. In the recent development of Autonomous Vehicles (AVs), human-machine trust and take-over strategy are important research contents, which is related to whether the driving system can make the most of its performance and ensure driving safety. At the same time, the unique advantages of Multi-Sensor Information Fusion (MSIF) also make it more and more applied to the auxiliary driving system. At present, research on MSIF applications in automated driving mainly focuses on external environment perception and map construction. However, there is little research on the application of MSIF in vehicle Human-Machine Interaction (HMI). Therefore, this paper analyzes the application of MSIF in trust detection and take-over HMI, proposes the advantages of applying MSIF to the HMI of AVs, and points out that the HMI design of intelligent vehicles in the future can enhance the application of MSIF. Dynamic monitoring of human-machine trust and dynamic adjustment of HMI strategy of the intelligent driving system are realized to achieve a better effect and further guarantee driving safety.

A Deep-Learning Approach to a Volumetric Radio Environment Map Construction for UAV-Assisted Networks

Providing global coverage for ubiquitous users is a key requirement of the fifth generation (5G) and beyond wireless technologies. This can be achieved by integrating airborne networks, such as unmanned aerial vehicles (UAVs) and satellite networks, with terrestrial networks. However, the deployment of airborne networks in a three-dimensional (3D) or volumetric space requires a new understanding of the propagation channel and its losses in both the areal and altitude dimensions. Despite significant research on radio environment map (REM) construction, much of it has been limited to two-dimensional (2D) contexts. This neglects the altitude-related characteristics of electromagnetic wave propagation and confines REMs to 2D formats, which limits the comprehensive and continuous visualization of propagation environment variation in spatial dimensions. This paper proposes a volumetric REM (VREM) construction approach to compute 3D propagation losses. The proposed approach addresses the limitations of existing approaches by learning the spatial correlation of wireless propagation channel characteristics and visualizing REM in areal and height/altitude dimensions using deep learning models. Specifically, the approach uses two deep learning-based models: volume-to-volume (Vol2Vol) VREM with 3D-generative adversarial networks and sliced VREM with altitude-aware spider-UNets. In both cases, knowledge of the propagation environment and transmitter locations in 3D space is used to capture the spatial and altitude dependency of the propagation channel’s characteristics. We developed the Addis dataset, a large REM dataset comprising 54,000 samples collected from the urban part of Addis Ababa, Ethiopia, to train the proposed models. Each sample of data comprises a 512-meter by 512-meter areal resolution with different 3D obstacles (buildings and terrain), 15 simulated propagation loss maps at every 3-meter altitude resolution, and 80 different 3D transmitter locations. The results of the training and testing of the proposed models reveal that the constructed VREMs are statistically comparable. In particular, the Vol2Vol approach has a minimum L1 loss of 0.01, which further decreases to 0.0084 as the line-of-sight (LoS) probability increases to 0.95.

Radio Environment Map Construction Based on Privacy-Centric Federated Learning

Radio Environment Map Construction Based on Privacy-Centric Federated Learning

A Novel Radio Environment Map Construction Scheme for Three-Dimensional and Full-Band for Modern Internet of Things Applications

A Novel Radio Environment Map Construction Scheme for Three-Dimensional and Full-Band for Modern Internet of Things Applications

Research on Visual SLAM Navigation Techniques for Dynamic Environments

Synchronous positioning and mapping mainly realize the functions of self-positioning and environment map construction for intelligent navigation technology. In order to solve the problems of low positioning accuracy and poor mapping effect of existing SLAM (simultaneous localization and mapping) systems in indoor dynamic environments and to improve the positioning accuracy, timeliness, and robustness of visual SLAM systems in dynamic environments, an improved visual SLAM method is proposed. Aiming at the inconsistency between the direction of dynamic objects and static background optical flow, this method adopts a high-real-time dynamic region mask detection algorithm to eliminate the feature points in the dynamic region mask, remove the camera motion optical flow according to the original feature information, and then cluster the optical flow amplitude of dynamic objects so as to realize the dynamic region mask detection and eliminate the dynamic signpost points combined with the polar geometric constraints. In order to verify the effectiveness of the improved algorithm, the three evaluation indexes of system accuracy, real-time performance, and the amount of drift are analyzed and verified, respectively, on the TUM dataset. The results show that the proposed algorithm not only has good real-time performance but also improves the accuracy of the system and reduces the amount of drift.

P‐2.3: Simultaneous localization and Mapping Method Based on Mechanical Lidar

With the progress of society and the development of science and technology, automatic driving, as the advanced stage of assisted driving, has become one of the most promising research directions in the future. The premise of realizing automatic driving is to accurately and efficiently perceive the surrounding environment, and SLAM technology can fuse multiple sensors to estimate the position and position of the vehicle, which is widely used in the construction of environmental maps. In real life, the environment in the process of car driving is often composed of dynamic people and vehicles. However, the SLAM algorithm based on static scene is easy to form wrong matching relationship when it performs point cloud matching in dynamic scene. Therefore, based on the dynamic object environment, this paper adopts the multi‐sensor fusion method, improves the frontend odometer on the basis of LIO‐SAM algorithm, adds semantic information to the lidar odometer, removes the dynamic object and improves the accuracy of point cloud registration. The experimental results show that the proposed method can filter the dynamic objects in the dropped point cloud data in the dynamic scene, and the mapping effect is better than that of the pure geometric method.

Research on Autonomous Navigation of Showroom Service Robot Based on ROS and Laser SLAM

As an important tool for guiding and introducing visitors to the exhibition hall, the robot needs to confront the complex environment that may exist and the constantly moving visitors. First of all, it is needed to have the ability to enable accurate positioning and navigation as well as dynamic obstacle avoidance. In recent years, simultaneous localization and mapping (SLAM) technology has been widely used to address localization and navigation issues in unknown indoor environments. Based on the robot operating system (ROS), this study collects environmental information by 2D LiDAR, and then combines the service robot's Inertial measurement unit (IMU), odometer, and other sensors to map the exhibition hall environment by the Cartographer SLAM algorithm. Path planning and dynamic obstacle avoidance functions are also implemented, in path planning, the algorithm is selected for global path planning and the Dynamic Window Approach (DWA) algorithm for local path planning. The environment map construction, path planning and dynamic obstacle avoidance functions are verified by simulation and in real environment. The average position deviation and standard deviation (SD) of the robot from the target point were less than 7 cm and less than 3 cm, respectively, when the robot was set to move at 0.5 m/s for the positioning test. The average heading deviation was less than 9° and SD was less than 3°. The robot's positioning and navigation can well meet the working requirements of the pavilion service robot.

A Kriging-Based Radio Environment Map Construction and Channel Estimation System in Threatening Environments

A Kriging-Based Radio Environment Map Construction and Channel Estimation System in Threatening Environments

UAV-Based Volumetric Measurements toward Radio Environment Map Construction and Analysis.

Unmanned aerial vehicle (UAV)-empowered communications have gained significant attention in recent years due to the promise of agile coverage provision for a large number of various mobile nodes on the ground and in three-dimensional (3D) space. Consequently, there is a need for efficient spectrum utilization in these dense aerial networks, which is characterized through radio environment maps (REMs), the construction of which is an important research area. Nevertheless, due to the difficult collection of radio frequency (RF) data, there are limited works that are based on real-world measurement campaigns. This paper presents a novel experimental setup that includes a constellation of three UAVs, the communication signals of which are measured by a software-defined radio (SDR) mounted on a separate UAV. It follows a trajectory that defines the REM's two-dimensional (2D) area on a plane, executed at four altitudes, to extend the REM to 3D. The measurements are then processed and their features (received mean power level, average difference of the mean power, percentage of meaningful correlations) are analyzed in the temporal, spatial, and frequency domains to determine the utilization of a 20 MHz band in the 2.4 GHz spectrum, as well as their variation with altitude. This analysis provides a base for research in reducing the amount of measurements (by identifying the regions of low and of high interest) and spectrum occupancy prediction for UAV-based communication coexistence.

Design and development of orchard autonomous navigation spray system.

Driven by the demand for efficient plant protection in orchards, the autonomous navigation system for orchards is hereby designed and developed in this study. According to the three modules of unmanned system “perception-decision-control,” the environment perception and map construction strategy based on 3D lidar is constructed for the complex environment in orchards. At the same time, millimeter-wave radar is further selected for multi-source information fusion for the perception of obstacles. The extraction of orchard navigation lines is achieved by formulating a four-step extraction strategy according to the obtained lidar data. Finally, aiming at the control problem of plant protection machine, the ADRC control strategy is adopted to enhance the noise immunity of the system. Different working conditions are designed in the experimental section for testing the obstacle avoidance performance and navigation accuracy of the autonomous navigation sprayer. The experimental results show that the unmanned vehicle can identify the obstacle quickly and make an emergency stop and find a rather narrow feasible area when a moving person or a different thin column is used as an obstacle. Many experiments have shown a safe distance for obstacle avoidance about 0.5 m, which meets the obstacle avoidance requirements. In the navigation accuracy experiment, the average navigation error in both experiments is within 15 cm, satisfying the requirements for orchard spray operation. A set of spray test experiments are designed in the final experimental part to further verify the feasibility of the system developed by the institute, and the coverage rate of the leaves of the canopy is about 50%.

Radio Environment Map Construction Based on Gaussian Process With Positional Uncertainty

Radio Environment Map (REM) is an effective tool for cognitive radio spectrum management. In the process of REM construction, the accurate location of equipment with measurement capability is often difficult to obtain, and inaccurate location information will have a more serious impact on mapping. In this letter, based on variational inference, we propose a Gaussian Process with positional uncertainty (GP-PU) algorithm for constructing a REM within the range of positional uncertainty, and by using the real dataset provided by the 2019 “Huawei” Mathematical Modeling Competition, the effectiveness of the algorithm is verified. In addition, we visualize REM to demonstrate that the GP-PU algorithm can obtain favorable REM. Finally, the simulation results show that the GP-PU algorithm outperforms the traditional method in terms of continuous hierarchical probability score (CRPS) and root mean square error (RMSE).

Genetic Algorithm-Based Optimization for Color Point Cloud Registration.

Point cloud registration is an important technique for 3D environment map construction. Traditional point cloud registration algorithms rely on color features or geometric features, which leave problems such as color affected by environmental lighting. This article introduced a color point cloud registration algorithm optimized by a genetic algorithm, which has good robustness for different lighting environments. We extracted the HSV color data from the point cloud color information and made the HSV distribution of the tangent plane continuous, and we used the genetic algorithm to optimize the point cloud color information consistently. The Gauss–Newton method was utilized to realize the optimal registration of color point clouds for the joint error function of color and geometry. The contribution of this study was that the genetic algorithm was used to optimize HSV color information of the point cloud and was applied to the point cloud registration algorithm, which reduces the influence of illumination on color information and improves the accuracy of registration. The experimental results showed that the square error of color information saturation and lightness optimized by the genetic algorithm was reduced by 14.07% and 37.16%, respectively. The color point cloud registration algorithm in this article was reduced by 12.53% on average compared with the optimal result algorithm RMSE.

Non-Uniform Input-Based Adaptive Growing Neural Gas for Unstructured Environment Map Construction

The research and development of special robots such as excavation robots is an important way to achieve safe and efficient production in coal mines. Affected by the unstructured environment such as complex working conditions and unsteady factor disturbances, the real-time construction of section environment maps that can accurately describe the environment and facilitate trajectory planning and decision making has become a key scientific problem to be solved as soon as possible. Therefore, non-uniform input based adaptive growing neural gas for unstructured environment map construction has been proposed. Considering complex load identification, real-time location identification, and the types of unsteady disturbance factors and working conditions, a set of environment identification models has been established based on a large amount of underground measured data training. These models can express whether the section environment has changed, as well as the type and magnitude of the change, to realize the overall knowledge extraction and parametric representation of the unstructured environment. Then, in order to solve the problems of inaccurate topology, excessive aging of connecting edges, and excessive deletion of nodes in non-uniform input environment, an adaptive growing neural gas algorithm based on non-uniform input environment (AGNG-NU) is proposed. Featured by a dynamic response deletion mechanism and adaptive adjustment mechanism of neuron parameters, the generated nodes and their topology can be dynamically adjusted according to the density of regional sample points. Several sets of non-uniform input environments are set to test the algorithm. The experimental results show that the topological maps established by AGNG-NU express clearer environmental information and, at the same time, the accuracy and distribution are improved by 8% and 15%, respectively, compared with the basic GNG algorithm. The accuracy and the distribution have also been significantly improved compared with other common SOM and GCS algorithms.

Radio Environment Map Construction Based on Spatial Statistics and Bayesian Hierarchical Model

Constructing an infrastructure for spectrum sensing and spectrum sharing on the cloud is a promising technology. In this paper, we propose an analysis framework based on spectrum data gathered by distributed sensors to construct the radio environment map (REM). To the best of our knowledge, this is the first attempt to leverage the power of Bayesian analysis and Markov chain Monte Carlo (MCMC) in REM. Specifically, a three-stage Bayesian hierarchical model (BHM) is established to imitate the spectrum data generation process under spatially correlated shadow fading. Parameters of BHM are estimated with the MCMC algorithm from data collected by the sensor network. Then, we address the space-dimension spectrum inference problem with the aim to interpolate the signal strength where there is no sensor node by composition sampling. At each point in the area of interest, the posterior predictive distribution of the receive signal strength can be obtained by kernel density smoother. We make spatial inference under two sensor location modes (square lattice located sensors and randomly located sensors) and two scenarios (with and without the information about the signal source), respectively. Simulation results demonstrate that although the randomly located sensors mode is suitable for parameter estimation, the inference performance is not better than the square lattice located sensors mode. Quantitative analysis of the inference performance confirms that the effectiveness of our data analysis framework is compelling.

Overview of Visual SLAM for Mobile Robots

Simultaneous Localization and Mapping (Simultaneous Localization and Mapping) technology refers to the technology of self-localization and construction of environmental maps based on visual sensors. It plays an important role in the field of autonomous mobile robots and autonomous vehicle navigation. This article introduces the classic framework and basic theory of visual SLAM, as well as the common methods and research progress of each part, enumerates the landmark achievements in the visual SLAM research process, and introduces the latest ORB-SLAM3. Finally, the current problems and future research directions of visual SLAM are proposed.

Radio environment map construction by adaptive ordinary Kriging algorithm based on affinity propagation clustering

In the era of 5G mobile communication, radio environment maps are increasingly viewed as a powerful weapon for the optimization of spectrum resources, especially in the field of autonomous vehicles. However, due to the constraint of limited resources when it comes to sensor networks, it is crucial to select a suitable scale of sensor measurements for radio environment map construction. This article proposes an adaptive ordinary Kriging algorithm based on affinity propagation clustering as a novel spatial interpolation method for the construction of the radio environment map, which can provide precise awareness of signal strength at locations where no measurements are available. Initially, a semivariogram is obtained from all the sensor measurements. Then, in order to select the minimum scale of measurements and at the same time guarantee accuracy, the affinity propagation clustering is introduced in the selection of sensors. Moreover, the sensor estimation groups are created based on the clustering result, and estimation results are obtained by ordinary Kriging. In the end, the simulation of the proposed algorithm is analyzed through comparisons with three conventional algorithms: inverse distance weighting, nearest neighbor, and ordinary Kriging. As a result, the conclusion can be drawn that the proposed algorithm is superior to others in accuracy as well as in efficiency.

Indoor 3D Semantic Robot VSLAM Based on Mask Regional Convolutional Neural Network

During the construction of indoor environmental semantic maps by robot Vision SLAM (VSLAM), there exist some problems such as low label classification accuracy and low precision under the situation of sparse feature points. In this case, this paper proposes an indoor three-dimensional semantic VSLAM algorithm based on Mask Regional Convolutional Neural Network (RCNN). Firstly, an Oriented FAST and a Rotated BRIEF (ORB) algorithms are used to extract image feature points. Secondly, a Random Sample Consensus (RANSAC) algorithm is employed to eliminate mismatched points and estimate camera position-pose changes. Then, a Mask RCNN algorithm is applied to make partial adjustments to its hyper parameter. A self-made data set is used to transfer learning, fulfilling real-time target detection and instance segmentation of a scene. A three-dimensional semantic map is constructed in combination with VSLAM algorithm. The semantic information in the environment not only improves the accuracy of VSLAM construction and positioning, but also reduces the impact of object movement on the construction by marking movable objects. Meanwhile, the VSLAM algorithm is used to calculate the positional constraints between objects and improve the accuracy of semantic understanding. Finally, by comparing with other methods, it demonstrates that this method is more correct and effective. It was also verified that the proposed method can accurately interpret the semantic information in environment for the construction of three-dimensional semantic maps.

Grid Map Construction and Terrain Prediction for Quadruped Robot Based on C-Terrain Path

In general, grid map based path planning algorithms are employed in the robotics arena. The algorithm uses a grid map to represent environmental information, standardized. Compared with feature maps and topological maps, the algorithm realizes the construction of environmental maps in a more direct way, and has the characteristics of fast, simple and efficient.The integration and prediction of terrain is an unavoidable problem and the traditional raster map prediction method is based on the research of the terrain data itself, and lacks dynamic supplement for the path planning process. When the environmental data changes, the classification algorithm can only be re-executed, and the past data is completely discarded. Since the planned path is unlikely to change, the terrain tends to be stable. To solve this problem, this paper proposes a concept of C(circular)-terrain band following path nodes and terrain construction and prediction methods. The C-Terrain method first obtains an ordered set of passing points at the initial moment, based on the complete path planning. Then an ordered sequence of influence function values is obtained, which depends on the selection of the terrain band and the adjustment of related parameters. Finally, regression methods such as machine learning are used to complete the prediction of the path and location terrain, and the unknown path and terrain are predicted. The experimental results prove the accuracy and practical value of the C-T method.

Radio Environment Map Construction Using Super-Resolution Imaging for Intelligent Transportation Systems

Radio environment map (REM) has emerged as a crucial technology to improve the robustness of intelligent transportation systems (ITS) by enhancing network planning and spectrum resource utilization. To construct a precise REM, optimizing deployment of sensor nodes and increasing spatial interpolation accuracy are two main directions. Given the deployment of sensor nodes, high resolution (HR) spatial interpolation would still bring about huge computing overhead, which is not practical for realtime applications. In order to improve the efficiency and accuracy of REM construction, we propose a super-resolution (SR) based REM construction method, which is composed of Kriging interpolation, dictionary learning and random forest. In our method, both low resolution (LR) and HR REM image sets are generated and trained to obtain a random forest model. With spectrum data from the limited number of sensor nodes, a SR REM can be acquired by the proposed method. Simulation results demonstrate that our method can greatly shorten the construction time of REM while maintaining high accuracy.

Radio Environment Map Construction by Kriging Algorithm Based on Mobile Crowd Sensing

In the IoT era, 5G will enable various IoT services such as broadband access everywhere, high user and devices mobility, and connectivity of massive number of devices. Radio environment map (REM) can be applied to improve the utilization of radio resources for the access control of IoT devices by allocating them reasonable wireless spectrum resources. However, the primary problem of constructing REM is how to collect the large scale of data. Mobile crowd sensing (MCS), leveraging the smart devices carried by ordinary people to collect information, is an effective solution for collecting the radio environment information for building the REM. In this paper, we build a REM collecting prototype system based on MCS to collect the data required by the radio environment information. However, limited by the budget of the platform, it is hard to recruit enough participants to join the sensing task to collect the radio environment information. This will make the radio environment information of the sensing area incomplete, which cannot describe the radio information accuracy. Considering that the Kriging algorithm has been widely used in geostatistics principle for spatial interpolation for Kriging giving the best unbiased estimate with minimized variance, we utilize the Kriging interpolation algorithm to infer complete radio environment information from collected sample radio environment information data. The interpolation performance is analyzed based on the collected sample radio environment information data. We demonstrate experiments to analyze the Kriging interpolation algorithm interpolation results and error and compared them with the nearest neighbor (NN) and the inverse distance weighting (IDW) interpolation algorithms. Experiment results show that the Kriging algorithm can be applied to infer radio environment information data based on the collected sample data and the Kriging interpolation has the least interpolation error.