Low-cost vector map assisted navigation strategy for autonomous vehicle

2018 ◽  
Author(s):  
Wenda Li ◽  
Xianjie Meng ◽  
Zheng Wang ◽  
Wenqi Fang ◽  
Jie Zou ◽  
...  
Keyword(s):  
Low Cost ◽  
Autonomous Vehicle ◽  
Assisted Navigation ◽  
Vector Map ◽  
Navigation Strategy ◽  
Cost Vector

scholarly journals Development and Experimental Evaluation of a Low-Cost Cooperative UAV Localization Network Prototype

2018 ◽  
Vol 7 (4) ◽  
pp. 42 ◽  
Author(s):  
Salil Goel ◽  
Allison Kealy ◽  
Bharat Lohani
Keyword(s):  
Information Sharing ◽  
Inertial Sensors ◽  
Low Cost ◽  
Wide Band ◽  
Cooperative Networks ◽  
Global Navigation Satellite Systems ◽  
Cooperative Localization ◽  
Cooperative Network ◽  
Precise Positioning ◽  
Assisted Navigation

Precise localization is one of the key requirements in the deployment of UAVs (Unmanned Aerial Vehicles) for any application including precision mapping, surveillance, assisted navigation, search and rescue. The need for precise positioning is even more relevant with the increasing automation in UAVs and growing interest in commercial UAV applications such as transport and delivery. In the near future, the airspace is expected to be occupied with a large number of unmanned as well as manned aircraft, a majority of which are expected to be operating autonomously. This paper develops a new cooperative localization prototype that utilizes information sharing among UAVs and static anchor nodes for precise positioning of the UAVs. The UAVs are retrofitted with low-cost sensors including a camera, GPS receiver, UWB (Ultra Wide Band) radio and low-cost inertial sensors. The performance of the low-cost prototype is evaluated in real-world conditions in partially and obscured GNSS (Global Navigation Satellite Systems) environments. The performance is analyzed for both centralized and distributed cooperative network designs. It is demonstrated that the developed system is capable of achieving navigation grade (2–4 m) accuracy in partially GNSS denied environments, provided a consistent communication in the cooperative network is available. Furthermore, this paper provides experimental validation that information sharing is beneficial to improve positioning performance even in ideal GNSS environments. The experiments demonstrate that the major challenges for low-cost cooperative networks are consistent connectivity among UAV platforms and sensor synchronization.

scholarly journals Nicking Endonuclease-Mediated Vector Construction Strategies for Plant Gene Functional Research

Plants ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1090
Author(s):  
Qi Gong ◽  
Bin Wang ◽  
Xubiao Lu ◽  
Jiantao Tan ◽  
Yuke Hou ◽  
...  
Keyword(s):  
Functional Genomics ◽  
Target Genes ◽  
Low Cost ◽  
Dna Assembly ◽  
Dna Fragments ◽  
Guide Rna ◽  
Vector Construction ◽  
Efficient Construction ◽  
Construction Strategies ◽  
Cost Vector

Plant genetic engineering vectors, such as RNA interference (RNAi) and CRISPR/Cas9 vectors, are important tools for plant functional genomics. Efficient construction of these functional vectors can facilitate the study of gene function. Although some methods for vector construction have been reported, their operations are still complicated and costly. Here, we describe a simpler and low-cost vector construction method by nicking endonucleases-mediated DNA assembly (NEMDA), which uses nicking endonucleases to generate single-strand overhanging complementary ends for rapid assembly of DNA fragments into plasmids. Using this approach, we rapidly completed the construction of four RNAi vectors and a CRISPR/Cas9 knockout vector with five single-guide RNA (sgRNA)-expression cassettes for multiplex genome editing, and successfully achieved the goal of decreasing the expression of the target genes and knocking out the target genes at the same time in rice. These results indicate the great potential of NEMDA in assembling DNA fragments and constructing plasmids for molecular biology and functional genomics.

GPS-INS Integration Algorithm for Autonomous Vehicle Using Loosely Coupled Integration Architecture

2013 ◽  
Vol 390 ◽  
pp. 506-511
Author(s):  
Rashid Iqbal ◽  
Zhong Jian Li ◽  
Khan Badshah
Keyword(s):  
Autonomous Vehicles ◽  
Low Cost ◽  
Autonomous Vehicle ◽  
Integrated System ◽  
Measurement Unit ◽  
Accurate Estimation ◽  
Initial Alignment ◽  
Integration Algorithm ◽  
Loosely Coupled ◽  
Integration Architecture

Inertial measurement unit (IMU) has been widely used for autonomous vehicles navigation. The accuracy of IMU specifies the performance of the inertial navigation system (INS).The errors in the INS are mainly due to the IMU inaccuracies, initial alignment, computational errors and approximations in the system equations. These errors are further integrated over time due to the dead-reckoning nature of the INS, which leads to unacceptable results. These errors need an accurate estimation for high precision navigation. INS is integrated with Global Positioning System (GPS) to estimate the errors and enhance the navigation capability of the INS. Linearized Kalman Filter (LKF) is proposed for estimating the errors in the low cost INS using Loosely Coupled integration approach, which is opted for its simplicity and robustness. Prediction part of the LKF is used during the GPS lag for errors estimation, which is found very effective for low cost sensors. The resulting GPS-INS integration algorithm is evaluated on simulated Autonomous vehicle trajectory, generated from 6-DOF model. The integrated system limits the attitude errors less than 0.1 deg and velocity errors of the order of 0.003 meter per second. Furthermore, it provides an optimal navigation solution than can be achieved from individual systems.

scholarly journals Low Cost Vector Signal Analyzer For Communication Experiments

2020 ◽  
Author(s):  
Frank Tuffner ◽  
Andrew Catellier ◽  
Robert Kubichek ◽  
John Pierre
Keyword(s):  
Low Cost ◽  
Vector Signal ◽  
Cost Vector

scholarly journals TRAFFIC SIGN INVENTORY FROM GOOGLE STREET VIEW IMAGES

2016 ◽  
Vol XLI-B4 ◽  
pp. 243-246
Author(s):  
Victor J. D. Tsai ◽  
Jyun-Han Chen ◽  
Hsun-Sheng Huang
Keyword(s):  
Intelligent Transportation Systems ◽  
Low Cost ◽  
Autonomous Vehicle ◽  
Transportation Systems ◽  
Traffic Sign ◽  
Google Street View ◽  
Street View ◽  
Sign Detection ◽  
Traffic Sign Detection ◽  
Detection And Recognition

Traffic sign detection and recognition (TSDR) has drawn considerable attention on developing intelligent transportation systems (ITS) and autonomous vehicle driving systems (AVDS) since 1980’s. Unlikely to the general TSDR systems that deal with real-time images captured by the in-vehicle cameras, this research aims on developing techniques for detecting, extracting, and positioning of traffic signs from Google Street View (GSV) images along user-selected routes for low-cost, volumetric and quick establishment of the traffic sign infrastructural database that may be associated with Google Maps. The framework and techniques employed in the proposed system are described.

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