scholarly journals Localization and Tracking of an Indoor Autonomous Vehicle Based on the Phase Difference of Passive UHF RFID Signals

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3286
Author(s):  
Yunlei Zhang ◽  
Xiaolin Gong ◽  
Kaihua Liu ◽  
Shuai Zhang
Keyword(s):  
Phase Difference ◽  
Radio Frequency Identification ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Localization Algorithm ◽  
Uhf Rfid ◽  
Dual Frequency ◽  
Tracking Accuracy ◽  
Vehicle Localization ◽  
Localization And Tracking

State-of-the-art radio frequency identification (RFID)-based indoor autonomous vehicles localization methods are mostly based on received signal strength indicator (RSSI) measurements. However, the accuracy of these methods is not high enough for real-world scenarios. To overcome this problem, a novel dual-frequency phase difference of arrival (PDOA) ranging-based indoor autonomous vehicle localization and tracking scheme was developed. Firstly, the method gets the distance between the RFID reader and the tag by dual-frequency PDOA ranging. Then, a maximum likelihood estimation and semi-definite programming (SDP)-based localization algorithm is utilized to calculate the position of the autonomous vehicles, which can mitigate the multipath ranging error and obtain a more accurate positioning result. Finally, vehicle traveling information and the position achieved by RFID localization are fused with a Kalman filter (KF). The proposed method can work in a low-density tag deployment environment. Simulation experiment results showed that the proposed vehicle localization and tracking method achieves centimeter-level mean tracking accuracy.

scholarly journals Performance Evaluation of a Novel Animals Tracking System based on UHF RFID Technology

2013 ◽  
Vol 9 (1) ◽  
pp. 4 ◽  
Author(s):  
Luca Catarinucci ◽  
Luigi Patrono
Keyword(s):  
Radio Frequency Identification ◽  
Tracking System ◽  
Near Field ◽  
Uhf Rfid ◽  
Wide Range ◽  
Frequency Identification ◽  
Set Up ◽  
Received Signal Strength Indication ◽  
Localization And Tracking ◽  
Localization Data

The adoption of solutions based on Radio Frequency IDentification technology in a wide range of contexts is a matter of fact. In many situations, such as the tracking of small-size living animals, the straightforward use of commercial systems does not ensure adequate performance. Consequently, both the RFID hardware and the software control platform should be tailored for the particular application. In this work, the specific requirements of Near Field Ultra High Frequency RFID reader antennas suitable for small-size animal localization and tracking are identified and a control system in a LabVIEW environment is designed. Afterwards, both hardware and software solutions have been implemented and validated. In particular, an algorithm based on the measured Received Signal Strength Indication, in order to obtain precise localization data, was developed and validated. Finally, the set-up of a first working prototype involving built-in-lab reader antennas has been completed and tested. The achieved results prove the effectiveness of the proposed tracking system.

scholarly journals A Fast and Accurate Approach to Multiple-Vehicle Localization and Tracking from Monocular Aerial Images

2021 ◽  
Vol 7 (12) ◽  
pp. 270
Author(s):  
Daniel Tøttrup ◽  
Stinus Lykke Skovgaard ◽  
Jonas le Fevre Sejersen ◽  
Rui Pimentel de Figueiredo
Keyword(s):  
Object Segmentation ◽  
Aerial Images ◽  
Simulation Environment ◽  
Tracking Accuracy ◽  
Video Streams ◽  
Simulation Data ◽  
Vehicle Localization ◽  
Top View ◽  
Tracking Objects ◽  
Localization And Tracking

In this work we present a novel end-to-end solution for tracking objects (i.e., vessels), using video streams from aerial drones, in dynamic maritime environments. Our method relies on deep features, which are learned using realistic simulation data, for robust object detection, segmentation and tracking. Furthermore, we propose the use of rotated bounding-box representations, which are computed by taking advantage of pixel-level object segmentation, for improved tracking accuracy, by reducing erroneous data associations during tracking, when combined with the appearance-based features. A thorough set of experiments and results obtained in a realistic shipyard simulation environment, demonstrate that our method can accurately, and fast detect and track dynamic objects seen from a top-view.

scholarly journals Autonomous Vehicle Localization with Prior Visual Point Cloud Map Constraints in GNSS-Challenged Environments

2021 ◽  
Vol 13 (3) ◽  
pp. 506
Author(s):  
Xiaohu Lin ◽  
Fuhong Wang ◽  
Bisheng Yang ◽  
Wanwei Zhang
Keyword(s):  
Autonomous Vehicles ◽  
Point Cloud ◽  
Autonomous Vehicle ◽  
Satellite System ◽  
Initial Position ◽  
Block Matching ◽  
Pose Prediction ◽  
Vehicle Localization ◽  
Accurate Localization ◽  
Mean Square Errors

Accurate vehicle ego-localization is key for autonomous vehicles to complete high-level navigation tasks. The state-of-the-art localization methods adopt visual and light detection and ranging (LiDAR) simultaneous localization and mapping (SLAM) to estimate the position of the vehicle. However, both of them may suffer from error accumulation due to long-term running without loop optimization or prior constraints. Actually, the vehicle cannot always return to the revisited location, which will cause errors to accumulate in Global Navigation Satellite System (GNSS)-challenged environments. To solve this problem, we proposed a novel localization method with prior dense visual point cloud map constraints generated by a stereo camera. Firstly, the semi-global-block-matching (SGBM) algorithm is adopted to estimate the visual point cloud of each frame and stereo visual odometry is used to provide the initial position for the current visual point cloud. Secondly, multiple filtering and adaptive prior map segmentation are performed on the prior dense visual point cloud map for fast matching and localization. Then, the current visual point cloud is matched with the candidate sub-map by normal distribution transformation (NDT). Finally, the matching result is used to update pose prediction based on the last frame for accurate localization. Comprehensive experiments were undertaken to validate the proposed method, showing that the root mean square errors (RMSEs) of translation and rotation are less than 5.59 m and 0.08°, respectively.

scholarly journals Project ARES: Driverless Transportation System. Challenges and Approaches in an Unstructured Road

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1753
Author(s):  
Pablo Marin-Plaza ◽  
David Yagüe ◽  
Francisco Royo ◽  
Miguel Ángel de Miguel ◽  
Francisco Miguel Moreno ◽  
...  
Keyword(s):  
Urban Areas ◽  
Autonomous Vehicles ◽  
Smart Cities ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Vehicle Localization ◽  
Transport Efficiency ◽  
The Road ◽  
Operational Design ◽  
Low Coverage

The expansion of electric vehicles in urban areas has paved the way toward the era of autonomous vehicles, improving the performance in smart cities and upgrading related driving problems. This field of research opens immediate applications in the tourism areas, airports or business centres by greatly improving transport efficiency and reducing repetitive human tasks. This project shows the problems derived from autonomous driving such as vehicle localization, low coverage of 4G/5G and GPS, detection of the road and navigable zones including intersections, detection of static and dynamic obstacles, longitudinal and lateral control and cybersecurity aspects. The approaches proposed in this article are sufficient to solve the operational design of the problems related to autonomous vehicle application in the special locations such as rough environment, high slopes and unstructured terrain without traffic rules.

scholarly journals An adaptive motion planning algorithm for obstacle avoidance in autonomous vehicle parking

2021 ◽  
Vol 10 (3) ◽  
pp. 687
Author(s):  
Naitik Nakrani ◽  
Maulin M. Joshi
Keyword(s):  
Motion Planning ◽  
Obstacle Avoidance ◽  
Vehicle Dynamics ◽  
Autonomous Vehicles ◽  
Intelligent Design ◽  
Autonomous Vehicle ◽  
Planning Techniques ◽  
Vehicle Localization ◽  
Adaptive Motion ◽  
Planning Algorithm

In the recent era, machine learning-based autonomous vehicle parking and obstacle avoidance navigation have drawn increased attention. An intelligent design is needed to solve the autonomous vehicles related problems. Presently, autonomous parking systems follow path planning techniques that generally do not possess a quality and a skill of natural adapting behavior of a human. Most of these designs are built on pre-defined and fixed criteria. It needs to be adaptive with respect to the vehicle dynamics. A novel adaptive motion planning algorithm is proposed in this paper that incorporates obstacle avoidance capability into a standalone parking controller that is kept adaptive to vehicle dimensions to provide human-like intelligence for parking problems. This model utilizes fuzzy membership thresholds concerning vehicle dimensions and vehicle localization to enhance the vehicle’s trajectory during parking when taking into consideration obstacles. It is generalized for all segments of cars, and simulation results prove the proposed algorithm’s effectiveness.

scholarly journals Multisensor-Based Target-Tracking Algorithm with Out-of-Sequence-Measurements in Cluttered Environments

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 4043 ◽  
Author(s):  
Ihsan Ullah ◽  
Muhammad Qureshi ◽  
Uzair Khan ◽  
Sufyan Memon ◽  
Yifang Shi ◽  
...  
Keyword(s):  
High Speed ◽  
Tracking System ◽  
Tracking Algorithm ◽  
Localization Algorithm ◽  
Target Engagement ◽  
Tracking Accuracy ◽  
Cluttered Environments ◽  
Remote Observation ◽  
Improved Performance ◽  
Localization And Tracking

A localization and tracking algorithm for an early-warning tracking system based on the information fusion of Infrared (IR) sensor and Laser Detection and Ranging (LADAR) is proposed. The proposed Kalman filter scheme incorporates Out-of-Sequence Measurements (OOSMs) to address long-range, high-speed incoming targets to be tracked by networked Remote Observation Sites (ROS) in cluttered environments. The Rauch–Tung–Striebel (RTS) fixed lag smoothing algorithm is employed in the proposed technique to further improve tracking accuracy, which, in turn, is used for target profiling and efficient filter initialization at the targeted platform. This efficient initialization increases the probability of target engagement by increasing the distance at which it can be effectively engaged. The increased target engagement range also reduces risk of any damage from debris of the engaged target. Performance of the proposed target localization algorithm with OOSM and RTS smoothing is evaluated in terms of root mean square error (RMSE) for both position and velocity, which accurately depicts the improved performance of the proposed algorithm in comparison with existing retrodiction-based OOSM filtering algorithms. The effects of assisted target state initialization at the targeted platform are also evaluated in terms of Time to Impact (TTI) and true track retention, which also depict the advantage of the proposed strategy.

scholarly journals IKULDAS: An Improved kNN-Based UHF RFID Indoor Localization Algorithm for Directional Radiation Scenario

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 968 ◽  
Author(s):  
Weiguang Shi ◽  
Jiangxia Du ◽  
Xiaowei Cao ◽  
Yang Yu ◽  
Yu Cao ◽  
...  
Keyword(s):  
Radio Frequency Identification ◽  
Nearest Neighbor ◽  
Indoor Localization ◽  
Rotation Angle ◽  
Localization Algorithm ◽  
Uhf Rfid ◽  
Estimation Model ◽  
K Value ◽  
Directional Radiation ◽  
Reader Antenna

Ultra high frequency radio frequency identification (UHF RFID)-based indoor localization technology has been a competitive candidate for context-awareness services. Previous works mainly utilize a simplified Friis transmission equation for simulating/rectifying received signal strength indicator (RSSI) values, in which the directional radiation of tag antenna and reader antenna was not fully considered, leading to unfavorable performance degradation. Moreover, a k-nearest neighbor (kNN) algorithm is widely used in existing systems, whereas the selection of an appropriate k value remains a critical issue. To solve such problems, this paper presents an improved kNN-based indoor localization algorithm for a directional radiation scenario, IKULDAS. Based on the gain features of dipole antenna and patch antenna, a novel RSSI estimation model is first established. By introducing the inclination angle and rotation angle to characterize the antenna postures, the gains of tag antenna and reader antenna referring to direct path and reflection paths are re-expressed. Then, three strategies are proposed and embedded into typical kNN for improving the localization performance. In IKULDAS, the optimal single fixed rotation angle is introduced for filtering a superior measurement and an NJW-based algorithm is advised for extracting nearest-neighbor reference tags. Furthermore, a dynamic mapping mechanism is proposed to accelerate the tracking process. Simulation results show that IKULDAS achieves a higher positioning accuracy and lower time consumption compared to other typical algorithms.

Autonomous vehicle self-localization in urban environments based on 3D curvature feature points – Monte Carlo localization

Robotica ◽  
2021 ◽  
pp. 1-17
Author(s):  
Qi Liu ◽  
Xiaoguang Di ◽  
Binfeng Xu
Keyword(s):  
Monte Carlo ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Mapping Method ◽  
Urban Environments ◽  
Feature Points ◽  
Localization Algorithm ◽  
Accurate Localization ◽  
Localization Algorithms ◽  
Monte Carlo Localization

Abstract This paper proposes a map-based localization system for autonomous vehicle self-localization in urban environments, which is composed of a pose graph mapping method and 3D curvature feature points – Monte Carlo Localization algorithm (3DCF-MCL). The advantage of 3DCF-MCL is that it combines the high accuracy of the 3D feature points registration and the robustness of particle filter. Experimental results show that 3DCF-MCL can provide an accurate localization for autonomous vehicles with the 3D point cloud map that generated by our mapping method. Compared with other map-based localization algorithms, it demonstrates that 3DCF-MCL outperforms them.

Vision-Based Navigation of Autonomous Vehicles in Roadway Environments with Unexpected Hazards

2019 ◽  
Vol 2673 (12) ◽  
pp. 494-507 ◽  
Author(s):  
Mhafuzul Islam ◽  
Mashrur Chowdhury ◽  
Hongda Li ◽  
Hongxin Hu
Keyword(s):  
Object Detection ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Semantic Segmentation ◽  
Steering Wheel ◽  
Potential Hazard ◽  
Driving System ◽  
Hazardous Object ◽  
Vision Based Navigation ◽  
Navigational System

Vision-based navigation of autonomous vehicles primarily depends on the deep neural network (DNN) based systems in which the controller obtains input from sensors/detectors, such as cameras, and produces a vehicle control output, such as a steering wheel angle to navigate the vehicle safely in a roadway traffic environment. Typically, these DNN-based systems in the autonomous vehicle are trained through supervised learning; however, recent studies show that a trained DNN-based system can be compromised by perturbation or adverse inputs. Similarly, this perturbation can be introduced into the DNN-based systems of autonomous vehicles by unexpected roadway hazards, such as debris or roadblocks. In this study, we first introduce a hazardous roadway environment that can compromise the DNN-based navigational system of an autonomous vehicle, and produce an incorrect steering wheel angle, which could cause crashes resulting in fatality or injury. Then, we develop a DNN-based autonomous vehicle driving system using object detection and semantic segmentation to mitigate the adverse effect of this type of hazard, which helps the autonomous vehicle to navigate safely around such hazards. We find that our developed DNN-based autonomous vehicle driving system, including hazardous object detection and semantic segmentation, improves the navigational ability of an autonomous vehicle to avoid a potential hazard by 21% compared with the traditional DNN-based autonomous vehicle driving system.

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