Sensor Fusion-Based Low-Cost Vehicle Localization System for Complex Urban Environments

2017 ◽  
Vol 18 (5) ◽  
pp. 1078-1086 ◽  
Author(s):  
Jae Kyu Suhr ◽  
Jeungin Jang ◽  
Daehong Min ◽  
Ho Gi Jung
Keyword(s):  
Sensor Fusion ◽  
Low Cost ◽  
Urban Environments ◽  
Vehicle Localization ◽  
Localization System

Low-cost localization system of impulsive sounds for urban environments

2014 ◽  
Author(s):  
Juan Manuel López R. ◽  
Jose Ignacio Marulanda B.
Keyword(s):  
Low Cost ◽  
Urban Environments ◽  
Localization System

Low-speed Unmanned Vehicle Localization Based on Sensor Fusion of Low-cost Stereo Camera and IMU

2021 ◽  
Author(s):  
Wei Wang ◽  
Hu Sun ◽  
Yuqiang Jin ◽  
Minglei Fu ◽  
Kun Li ◽  
...  
Keyword(s):  
Sensor Fusion ◽  
Low Cost ◽  
Vehicle Localization ◽  
Unmanned Vehicle ◽  
Stereo Camera ◽  
Low Speed

scholarly journals FAST Pre-Filtering-Based Real Time Road Sign Detection for Low-Cost Vehicle Localization

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3590 ◽  
Author(s):  
Kyoungtaek Choi ◽  
Jae Kyu Suhr ◽  
Ho Gi Jung
Keyword(s):  
Embedded System ◽  
Processing Time ◽  
State Of The Art ◽  
Low Cost ◽  
Position Estimation ◽  
The State ◽  
Vehicle Localization ◽  
Localization System ◽  
Road Sign ◽  
The Cost

In order to overcome the limitations of GNSS/INS and to keep the cost affordable for mass-produced vehicles, a precise localization system fusing the estimated vehicle positions from low-cost GNSS/INS and low-cost perception sensors is being developed. For vehicle position estimation, a perception sensor detects a road facility and uses it as a landmark. For this localization system, this paper proposes a method to detect a road sign as a landmark using a monocular camera whose cost is relatively low compared to other perception sensors. Since the inside pattern and aspect ratio of a road sign are various, the proposed method is based on the part-based approach that detects corners and combines them to detect a road sign. While the recall, precision, and processing time of the state of the art detector based on a convolutional neural network are 99.63%, 98.16%, and 4802 ms respectively, the recall, precision, and processing time of the proposed method are 97.48%, 98.78%, and 66.7 ms, respectively. The detection performance of the proposed method is as good as that of the state of the art detector and its processing time is drastically reduced to be applicable for an embedded system.

Lane Marking-Based Vehicle Localization Using Low-Cost GPS and Open Source Map

2015 ◽  
Vol 03 (04) ◽  
pp. 239-251 ◽  
Author(s):  
Wenjie Lu ◽  
Sergio A. Rodríguez F. ◽  
Emmanuel Seignez ◽  
Roger Reynaud
Keyword(s):  
Low Cost ◽  
Autonomous Vehicle ◽  
Estimation Method ◽  
Dynamic Environment ◽  
Kernel Estimation ◽  
Complex Problem ◽  
Urban Environments ◽  
Vehicle Localization ◽  
Open Map ◽  
High Level

Autonomous Vehicle applications and Advanced Driving Assistance Systems (ADAS) need scene understanding processes, allowing high-level systems to carry out decision. For such systems, the localization of a vehicle evolving in a structured dynamic environment constitutes a complex problem of crucial importance. However, the low accuracy of the global positioning system (GPS) system in urban environments makes its localization unreliable for further treatments. The combination of GPS data and additional sensors (WSS, IMU or Camera) can improve the localization precision. More and more, digital maps are also used in this process. Generally, these maps are customized or built for a specific application, asking high-cost to design and upgrade. In this paper, we propose a low-cost localization system based on camera, GPS and open map. Starting from the road marking, detected by a multi-kernel estimation method, a particle filter generates the samples taking advantage of lane markings to predict the most probable trajectory of the vehicle and the low-cost GPS position. Then, the accuracy of the localization is improved using an open map. This process was validated through several scenarios with a public database and our experimental platform.

Towards a low-cost localization system for small ruminants

2021 ◽  
Vol 185 ◽  
pp. 106172
Author(s):  
Rui Guedes ◽  
Paulo Pedreiras ◽  
Luís Nóbrega ◽  
Pedro Gonçalves
Keyword(s):  
Low Cost ◽  
Small Ruminants ◽  
Localization System
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