Vehicle Location Research Articles

Joint RSU and agent vehicle cooperative localization using mmWave sensing

In vehicle networks, accurate vehicle localization is crucial. This paper proposes a joint roadside unit (RSU) and agent vehicles cooperative localization framework based on dual-function radar-communication (DFRC) technology. It utilizes unscented Kalman filtering (UKF) to process DFRC signals and obtain vehicle status information. To improve the angle prediction accuracy of the agent vehicle, an angle fusion estimation scheme based on the maximum likelihood algorithm is proposed. Furthermore, a weighted method is introduced within the joint RSU and agent vehicle cooperative localization to enhance vehicle localization accuracy. Experimental results demonstrate that the proposed angle fusion scheme reduces angle estimation error, and the joint RSU and agent vehicle localization framework significantly improves vehicle localization accuracy.

Online Calibration of Inertial Sensors Based on Error Backpropagation

Global satellite navigation systems (GNSSs) are the most-used technology for the localization of vehicles in the outdoor environment, but in the case of a densely built-up area or during passage through a tunnel, the satellite signal is not available or has poor quality. Inertial navigation systems (INSs) allow localization dead reckoning, but they have an integration error that grows over time. Inexpensive inertial measurement units (IMUs) are subject to thermal-dependent error and must be calibrated almost continuously. This article proposes a novel method of online (continuous) calibration of inertial sensors with the aid of the data from the GNSS receiver during the vehicle’s route. We performed data fusion using an extended Kalman filter (EKF) and calibrated the input sensors through error backpropagation. The algorithm thus calibrates the INS sensors while the GNSS receiver signal is good, and after a GNSS failure, for example in tunnels, the position is predicted only by low-cost inertial sensors. Such an approach significantly improved the localization precision in comparison with offline calibrated inertial localization with the same sensors.

Smart Fuel Monitoring for Agricultural Fleet Operations Using Tilt Sensors and Cloud Data Processing

This paper presents a novel Internet of Things (IoT)-based fuel monitoring and tracking system designed to address limitations of traditional fuel gauges and enhance fleet management efficiency. The system leverages an accelerometer and Arduino microcontroller to measure fuel level by detecting tilt angles within the tank. This data, along with real-time location from GPS, is transmitted to a cloud platform for processing and visualization. A mobile application, "Tracer," empowers users with insights into fuel level, location on a map, and distance traveled. Graphical fuel consumption patterns further aid in optimization strategies. This solution offers real-time data for improved fuel efficiency, proactive route planning, and overall fleet management. The proposed system is successfully implemented IOT based technology. This sys in Ada fruit IO cloud issued to provide information about vehicle location, fuel level in tank and distance travelled by the vehicle.

A Lightweight, Centralized, Collaborative, Truncated Signed Distance Function-Based Dense Simultaneous Localization and Mapping System for Multiple Mobile Vehicles.

Simultaneous Localization And Mapping (SLAM) algorithms play a critical role in autonomous exploration tasks requiring mobile robots to autonomously explore and gather information in unknown or hazardous environments where human access may be difficult or dangerous. However, due to the resource-constrained nature of mobile robots, they are hindered from performing long-term and large-scale tasks. In this paper, we propose an efficient multi-robot dense SLAM system that utilizes a centralized structure to alleviate the computational and memory burdens on the agents (i.e. mobile robots). To enable real-time dense mapping of the agent, we design a lightweight and accurate dense mapping method. On the server, to find correct loop closure inliers, we design a novel loop closure detection method based on both visual and dense geometric information. To correct the drifted poses of the agents, we integrate the dense geometric information along with the trajectory information into a multi-robot pose graph optimization problem. Experiments based on pre-recorded datasets have demonstrated our system's efficiency and accuracy. Real-world online deployment of our system on the mobile vehicles achieved a dense mapping update rate of ∼14 frames per second (fps), a onboard mapping RAM usage of ∼3.4%, and a bandwidth usage of ∼302 KB/s with a Jetson Xavier NX.

The INS/GPS integrated navigation based on autonomous underwater vehicle using modified extended Kalman filter

Ensuring highhtab accuracy and robustness remains a paramount concern in the realm of underwater positioning and navigation research. This paper proposes a filtering algorithm grounded in Inertial Measurement Unit/Global Positioning System (INS/GPS) integrated navigation to effectively address the challenges posed by non-Gaussian noise, stemming from outliers and measurement inaccuracies. Currently, most filtering algorithms are developed based on the criteria of minimum mean square error (MMSE) or maximum entropy criteria (MCC). To address the challenge of handling complex non-Gaussian noises, this paper uses the Minimum Error Entropy Criterion (MEE) and Extended Kalman Filter (EKF) with Kernel Risk-Sensitive Loss (KRSL) instead of MMSE or MCC to develop an online filtering algorithm with a recursive process, and adjusts the kernel size of MEE within a reasonable range by constructing an adaptive factor to deal with outliers generated during measurement and excessive convergence caused by the inapplicability of the kernel size. Through extensive target tracking simulations and surface Autonomous Underwater Vehicle (AUV) localization experiments, results demonstrate the efficacy of the proposed algorithm. Notably, the algorithm optimizes kernel sizes for different types of noises, ensuring robust state estimation and rapid convergence without compromising filtering accuracy.

Research on the Technology and Application of Remote Wireless Transmission of Digital Image Signal

Abstract. With the increasing demand for remote real-time high-quality video transmission, especially in the context of large area applications of unmanned aerial vehicle (UAV) and remote location instant live broadcast, traditional wired transmission methods have been surpassed by digital transmission methods due to their superior performance in image quality, transmission distance and delay. Therefore, this study focuses on the application of digital image wireless transmission in remote scenarios such as UAVs, including key digital transmission technologies such as Orthogonal Frequency Division Multiplexing (OFDM) and Coded Orthogonal Frequency Division Multiplexing (COFDM), as well as public network link communication technologies such as 4G and 5G, by means of a literature study and a case study. It also analyzes the development of WiFi, Lightbridge and OcuSync graphics transmission systems using DJI brand products as case studies. The analysis shows that digital image wireless transmission, especially COFDM and advanced proprietary systems such as DJI's OcuSync, has significant advantages in terms of transmission distance, interference immunity, encryption techniques, and reusability. However, factors such as building obstruction, frequency interference, and weather conditions can affect the transmission quality. To mitigate these challenges, solutions are proposed, such as auxiliary signal enhancement systems, adaptive frequency hopping and software-defined radio technologies.

ROV localization in the nuclear reactor pressure vessel using LSTM and an improved adaptive Kalman Filter

In this paper, a remotely operated vehicle (ROV) localization algorithm for nuclear reactor pressure vessels is introduced. Traditional machine vision localization suffers from visual blind zones. To address this issue, a long short-term memory (LSTM) network was employed to obtain the ROV’s position. To further enhance the accuracy, an improved Sage-Husa adaptive Kalman Filter (SHAKF) was applied to eliminate cumulative errors from LSTM. Finally, upper-diagonal decomposition and confidence checks were used to enhance the traditional SHAKF, addressing issues associated with the non-positive definite error covariance matrix and sensor limitations. Simulated and experimental results demonstrated that the LSTM network significantly reduced the cumulative error from trajectory projection localization. The root mean square error of the localization results was reduced from 51.776 to 12.808 mm due to the improvements made to the traditional SHAKF. Additionally, the improved SHAKF effectively minimized the cumulative LSTM localization error.

Joint Active and Passive Beamforming for Vehicle Localization With Reconfigurable Intelligent Surfaces

Joint Active and Passive Beamforming for Vehicle Localization With Reconfigurable Intelligent Surfaces

5G Networks Enabling Cooperative Autonomous Vehicle Localization: A Survey

5G Networks Enabling Cooperative Autonomous Vehicle Localization: A Survey

5G/GNSS Integrated Vehicle Localization With Adaptive Step Size Kalman Filter

5G/GNSS Integrated Vehicle Localization With Adaptive Step Size Kalman Filter

Terrain preview detection system based on loosely coupled and tightly coupled fusion with lidar and IMU

The ride comfort of emergency rescue vehicles is considerably affected by the terrain preview accuracy. In this paper, a robust, precise terrain preview detection system that integrates lidar with IMU is presented based on loosely and tightly coupled fusion. To evaluate the terrain measurement error, lidar measurement error and vehicle motion error are considered to achieve centimeter-level mapping and localization accuracy in disparate scenes. To reduce the localization cumulative error, the frontend laser odometer and backend factor graph optimization are innovatively combined, and the cumulative error compensation strategy is designed. Experimental validation is conducted in different scenes to assess the performance of the prototype and to validate the simulation results. The vehicle localization accuracy is greater than 4–8 cm, and the simulated and measured terrain elevation waveforms exhibit strong correlations, which verifies the reliability and validity of the simulation method. The elevation measurement accuracy reaches 87 %.

Vehicle Localization Method in Complex SAR Images Based on Feature Reconstruction and Aggregation.

Due to the small size of vehicle targets, complex background environments, and the discrete scattering characteristics of high-resolution synthetic aperture radar (SAR) images, existing deep learning networks face challenges in extracting high-quality vehicle features from SAR images, which impacts vehicle localization accuracy. To address this issue, this paper proposes a vehicle localization method for SAR images based on feature reconstruction and aggregation with rotating boxes. Specifically, our method first employs a backbone network that integrates the space-channel reconfiguration module (SCRM), which contains spatial and channel attention mechanisms specifically designed for SAR images to extract features. The network then connects a progressive cross-fusion mechanism (PCFM) that effectively combines multi-view features from different feature layers, enhancing the information content of feature maps and improving feature representation quality. Finally, these features containing a large receptive field region and enhanced rich contextual information are input into a rotating box vehicle detection head, which effectively reduces false alarms and missed detections. Experiments on a complex scene SAR image vehicle dataset demonstrate that the proposed method significantly improves vehicle localization accuracy. Our method achieves state-of-the-art performance, which demonstrates the superiority and effectiveness of the proposed method.

Optimized Factor Graph for Tightly-Coupled LiDAR/IMU Localization in Underground Parking Garages

Abstract. To address the challenge of intelligent vehicle localization in underground parking structures due to the loss of GNSS signals, this paper introduces a method to address this issue by developing a novel localization framework known as GF-LIO, which denotes a tightly-coupled fusion of LiDAR and IMU data, innovatively combining the Interactive Extended State Kalman Filter (IESKF) with a factor graph to enhance the localization process and solve the problem of GNSS signal loss in underground parking lots. The GF-LIO model commences with a strategic feature selection process, facilitated by a greedy algorithm that prioritizes environmental cues within the point cloud data. This method effectively filters out redundant features, thereby enhancing the saliency of retained features and subsequently improving the robustness of the localization process. Following feature selection, the model integrates LiDAR and IMU measurements utilizing the IESKF algorithm, ensuring a cohesive fusion of sensor data and bolstering attitude estimation accuracy. The culmination of the GF-LIO framework involves factor graph optimization, a sophisticated technique that synthesizes LiDAR odometry, IMU pre-integration factors, and loop closure detection factors. This optimization step enhances the overall precision and consistency of the localization process, resulting in superior performance compared to existing methodologies. Experimental evaluations conducted within underground parking environments corroborate the efficacy of the GF-LIO model. Comparative analyses against established approaches such as A-LOAM, LeGO-LOAM, LIO-LOAM, and FAST-LIO demonstrate a notable performance improvement exceeding 12.53%. The proposed model adeptly integrates domain-specific environmental characteristics with multi-sensor data, thereby facilitating precise localization and map construction tasks for intelligent vehicle navigating within the intricate confines of subterranean parking structures.

OMC-VINS: A Bionic Skylight Orientation and Vision-Based Metric Constrains Proposal for Vehicle Location in Challenging Environment

OMC-VINS: A Bionic Skylight Orientation and Vision-Based Metric Constrains Proposal for Vehicle Location in Challenging Environment

Dynamic indoor mapping for AVP: Crowdsourcing mapping without prior maps

AbstractHigh‐definition maps are essential for autonomous vehicle navigation, but indoor parking lots remain poorly mapped due to high costs. To address this, a crowdsourcing model gathers data from consumer‐grade sensors in mass‐produced vehicles to create semantic maps. Indoor parking lots lack GNSS signals, and most of them do not have high‐definition maps or navigation maps as references, making it difficult to ensure the accuracy of the final mapping results. Additionally, the semantic information of indoor parking lots is relatively limited, and the geometric features are overly similar, which significantly impacts the accuracy of point cloud registration. Therefore, this article proposes a crowdsourcing‐based approach, where vehicles generate local semantic maps at the client end and upload them to the cloud. Leveraging the scene characteristics of indoor parking lots, the cloud optimizes and fits a large amount of crowdsourced data to obtain a high‐precision base map without prior information. Enhanced ICP point cloud registration merges subsequent maps with the base. Additionally, parking space occupancy information is provided. This map can furnish the necessary information for Autonomous Valet Parking (AVP) tasks. Evaluation on the BEVIS dataset shows a root mean square error of 0.482446 m for vehicle localization on the cloud‐based map.

Nonlinear model predictive dynamic positioning of a remotely operated vehicle with wave disturbance preview

Exploiting preview information of disturbances within robot control is an effective method of handling state perturbations. For underwater vehicles operating in wave-dominated environments, disturbances significantly influence vehicle response and pose a threat to operational safety. In consideration of this, a complete end-to-end control architecture is developed in this work for disturbance rejection during station keeping tasks under wave perturbations, encompassing a nonlinear model predictive controller (NMPC) combined with a deterministic sea wave predictor (DSWP). The wave predictor exploits a continuous measurement of the wave elevation at a location upstream of the vehicle to form a forecast of the temporal evolution of the wave elevation at the vehicle location. The predicted wave parameters are then used to estimate the impending wave-induced hydrodynamic loading, enabling explicit consideration of accurate short-term disturbance forecasts within the controller, ensuring this preview is incorporated in the optimised control sequence. Experimental testing confirms the validity of the wave predictor, producing RMSE as low as 0.017 m. The proposed strategy is then simulated under various wave conditions, optimising control actions inclusive of the preview information. The NMPC outperforms a similar disturbance mitigating feed-forward controller, with average improvements of up to 52% and is found to perform best even with noisy, lower accuracy wave predictions, as well as with communication time delays, providing a high degree of robustness. This demonstrates the potential of the proposed control framework to effectively tackle disturbance mitigation of underwater vehicles subject to large magnitude wave disturbances, expanding the operational envelop of autonomous systems towards forbidding ocean environments.

A modified noise prediction model based on vehicles’ random probability distribution for signalized and main road priority-controlled intersections

A modified noise prediction model based on vehicles’ random probability distribution is proposed to address the difficulty of noise prediction caused by various traffic characteristics of different types of intersections. Monte Carlo simulation is used to generate specific vehicle locations with the assumption of time headway is Burr distribution, and traffic noise can be calculated based on the energy superposition principle. The model has been validated as the average absolute error for Leq of 1.4 dB(A) at both signalized and main road priority-controlled intersections. Applications of two types of intersections are presented. Different noise impacts are indicated in the two intersection modes, as the signalized intersection reduces noise value by 2.1 dB compared with a 0.3 dB increase at priority-controlled intersections with speeds varying from 30 to 50 km/h. Higher speeds result in more concentrated noise values across flow ratios at signalized intersections, while the opposite trend is observed at priority-controlled intersections. Flow ratios of two axes, speed, and receiver locations play different roles in the noise effect of two intersection modes.

Moving sound source localization in 3D space based on time delay and energy ratio technique

Moving sound source localization has numerous applications in interdisciplinary realms such as in teleconferencing, in defense, in robotics, and fault localization in vehicles. Localization of moving source is more challenging than localizing stationary sources because the motion disturbs the sound field and manipulates the frequency and amplitude of the acoustic pressure at recording location, which hampers the accuracy of source localization. Existing moving source localization techniques have numerous restrictions such as source trajectory should be known beforehand as a straight line perpendicular to the array geometry, source should be in same plane or parallel to the microphone array plane, and source velocity should be known. Moreover, when the source elevation is high, these techniques give high error. This study proposes the time delay and energy ratio based real-time moving sound source localization technique that is free from above-mentioned restrictions and can track the source's 3D coordinates accurately. Six microphones are used to localize a moving source by processing the time delay and energy ratios between sensor pairs. The root mean square error found for low source speed and high source speed conditions are 0.73 m and 1.8 m respectively.

Designing a practical physical framework for AI-enhanced sound detection and localization in vehicles

This paper presents the practical framework of an AI-implemented system currently in development for the detection and localization of external sound events to the vehicle. We delve into the initial block of the automotive project, analyzing sounds of interest and the noise we encounter, addressing the strategic arrangement of the four microphones on the sides of the vehicle, conceived as the sound input system. This configuration not only optimizes signal capture but also acts as sensitive ears, allowing the system to effectively analyze the acoustic environment. To mitigate wind interference, we have designed a microphone placement system that considers the vehicle's aerodynamics. The strategic location and implementation of aerodynamic protectors significantly minimize the impact of wind on the microphones, ensuring a sharper capture of acoustic signals. Complementing this arrangement, we apply advanced noise elimination processes, further enhancing the quality of the captured signals. Although in its early stages, this project aspires to provide effective solutions for vehicular acoustic perception, marking the beginning of a journey towards innovation in the detection and localization of sounds in mobile environments.

Fast and Robust 6-DoF LiDAR-Based Localization of an Autonomous Vehicle Against Sensor Inaccuracy

Fast and Robust 6-DoF LiDAR-Based Localization of an Autonomous Vehicle Against Sensor Inaccuracy