A vehicle localization algorithm based on histogram block characteristic value clustering

2014 ◽  
Author(s):  
Dan Zhang ◽  
Ningsheng Gong ◽  
Jingwei Jiang ◽  
Kaijiao Wu
Keyword(s):  
Localization Algorithm ◽  
Vehicle Localization ◽  
Characteristic Value

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 LOCALIZATION OF A CAR BASED ON MULTI-SENSOR FUSION

2018 ◽  
Vol XLII-1 ◽  
pp. 247-250 ◽  
Author(s):  
H. Kim ◽  
I. Lee
Keyword(s):  
Sensor Fusion ◽  
Visual Odometry ◽  
Sensor Data ◽  
Localization Algorithm ◽  
Vision Sensor ◽  
High Definition ◽  
Vehicle Localization ◽  
Speed Sensor ◽  
Time Update ◽  
Vehicle Sensors

<p><strong>Abstract.</strong> The vehicle localization is an essential component for stable autonomous car operation. There are many algorithms for the vehicle localization. However, it still needs much improvement in terms of its accuracy and cost. In this paper, sensor fusion based localization algorithm is used for solving this problem. Our sensor system is composed of in-vehicle sensors, GPS and vision sensors. The localization algorithm is based on extended Kalman filter and it has time update step and measurement update step. In the time update step, in-vehicle sensors are used such as yaw-rate and speed sensor. And GPS and vision sensor information are used to update the vehicle position in the measurement update step.We use visual odometry library to process vision sensor data and generate the moving distance and direction of the car. Especially, when performing visual odometry we use georeferenced image database to reduce the error accumulation. Through the experiments, the proposed localization algorithm is verified and evaluated. The RMS errors of the estimated result from the proposed algorithm are about 4.3<span class="thinspace"></span>m. This result shows about 40<span class="thinspace"></span>% improvement in accuracy even in comparison with the result from the GPS only method. It shows the possibility to use proposed localization algorithm. However, it is still necessary to improve the accuracy for applying this algorithm to the autonomous car. Therefore, we plan to use multiple cameras (rear cameras or AVM cameras) and more information such as high-definition map or V2X communication. And the filter and error modelling also need to be changed for the better results.</p>

Stereo Vision Based Localization Method for Intelligent Vehicle

2013 ◽  
Vol 694-697 ◽  
pp. 1931-1936
Author(s):  
Feng Ping Cao ◽  
Rong Ben Wang ◽  
Liang Xiu Zhang
Keyword(s):  
Stereo Vision ◽  
Probability Model ◽  
Three Dimensional ◽  
Dead Reckoning ◽  
Intelligent Vehicle ◽  
Localization Algorithm ◽  
Vehicle Localization ◽  
Localization Method ◽  
Localization And Mapping ◽  
Slam Algorithm

In order to overcome the accumulated error in traditional localization methods for intelligent vehicle such as dead reckoning and visual odometry, a simultaneous localization and mapping(SLAM) algorithm based on stereo vision was presented in the paper. Firstly, the interrelated elements in the localization method were defined, and the probability model for intelligent vehicle localization was proposed, then the motion and observation model were established, and the detailed implementation of the introduced localization algorithm was given. Finally, an experiment was designed to confirm the effectiveness of the proposed method. Experimental results indicate that the algorithm can realize three-dimensional motion estimation of intelligent vehicle and can improve the positioning precision effectively.

An Efficient Area-Based Localization Algorithm for Wireless Sensor Networks

2015 ◽  
Vol 10 (10) ◽  
pp. 1062
Author(s):  
A. Mesmoudi ◽  
Mohammed Feham ◽  
Nabila Labraoui ◽  
Chakib Bekara
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Localization Algorithm
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