A light-weight yet accurate localization system for autonomous cars in large-scale and complex environments

2016 ◽  
Author(s):  
Lucas De Paula Veronese ◽  
Jose Guivant ◽  
Fernando A. Auat Cheein ◽  
Thiago Oliveira-Santos ◽  
Filipe Mutz ◽  
...  
Keyword(s):  
Large Scale ◽  
Light Weight ◽  
Complex Environments ◽  
Localization System ◽  
Accurate Localization ◽  
Autonomous Cars

scholarly journals GNSS RUMS: GNSS Realistic Urban Multiagent Simulator for Collaborative Positioning Research

2021 ◽  
Vol 13 (4) ◽  
pp. 544
Author(s):  
Guohao Zhang ◽  
Bing Xu ◽  
Hoi-Fung Ng ◽  
Li-Ta Hsu
Keyword(s):  
Urban Area ◽  
Intelligent Transportation Systems ◽  
Urban Areas ◽  
Large Scale ◽  
Transportation Systems ◽  
Accurate Localization ◽  
Gnss Receivers ◽  
Multipath Signal ◽  
Scale Test ◽  
Error Behavior

Accurate localization of road agents (GNSS receivers) is the basis of intelligent transportation systems, which is still difficult to achieve for GNSS positioning in urban areas due to the signal interferences from buildings. Various collaborative positioning techniques were recently developed to improve the positioning performance by the aid from neighboring agents. However, it is still challenging to study their performances comprehensively. The GNSS measurement error behavior is complicated in urban areas and unable to be represented by naive models. On the other hand, real experiments requiring numbers of devices are difficult to conduct, especially for a large-scale test. Therefore, a GNSS realistic urban measurement simulator is developed to provide measurements for collaborative positioning studies. The proposed simulator employs a ray-tracing technique searching for all possible interferences in the urban area. Then, it categorizes them into direct, reflected, diffracted, and multipath signal to simulate the pseudorange, C/N0, and Doppler shift measurements correspondingly. The performance of the proposed simulator is validated through real experimental comparisons with different scenarios based on commercial-grade receivers. The proposed simulator is also applied with different positioning algorithms, which verifies it is sophisticated enough for the collaborative positioning studies in the urban area.

scholarly journals CHORD: Distributed Data-sharing via Hybrid ROS 1 and 2 for Multi-robot Exploration of Large-scale Complex Environments

2021 ◽  
pp. 1-1
Author(s):  
Muhammad Fadhil Ginting ◽  
Kyohei Otsu ◽  
Jeffrey Edlund ◽  
Jay Gao ◽  
Ali-akbar Agha-mohammadi
Keyword(s):  
Data Sharing ◽  
Large Scale ◽  
Distributed Data ◽  
Complex Environments ◽  
Multi Robot

scholarly journals A High-Speed, Light-Weight Scalar Magnetometer Bird for km Scale UAV Magnetic Surveying: On Sensor Choice, Bird Design, and Quality of Output Data

2021 ◽  
Vol 13 (4) ◽  
pp. 649
Author(s):  
Arne Døssing ◽  
Eduardo Lima Simoes da Silva ◽  
Guillaume Martelet ◽  
Thorkild Maack Rasmussen ◽  
Eric Gloaguen ◽  
...  
Keyword(s):  
High Speed ◽  
Large Scale ◽  
Signal To Noise Ratio ◽  
Regional Scale ◽  
Weather Conditions ◽  
Light Weight ◽  
Output Data ◽  
Efficient Manner ◽  
Flexible System ◽  
Scalar Data

Magnetic surveying is a widely used and cost-efficient remote sensing method for the detection of subsurface structures at all scales. Traditionally, magnetic surveying has been conducted as ground or airborne surveys, which are cheap and provide large-scale consistent data coverage, respectively. However, ground surveys are often incomplete and slow, whereas airborne surveys suffer from being inflexible, expensive and characterized by a reduced signal-to-noise ratio, due to increased sensor-to-source distance. With the rise of reliable and affordable survey-grade Unmanned Aerial Vehicles (UAVs), and the developments of light-weight magnetometers, the shortcomings of traditional magnetic surveying systems may be bypassed by a carefully designed UAV-borne magnetometer system. Here, we present a study on the development and testing of a light-weight scalar field UAV-integrated magnetometer bird system (the CMAGTRES-S100). The idea behind the CMAGTRES-S100 is the need for a high-speed and flexible system that is easily transported in the field without a car, deployable in most terrain and weather conditions, and provides high-quality scalar data in an operationally efficient manner and at ranges comparable to sub-regional scale helicopter-borne magnetic surveys. We discuss various steps in the development, including (i) choice of sensor based on sensor specifications and sensor stability tests, (ii) design considerations of the bird, (iii) operational efficiency and flexibility and (iv) output data quality. The current CMAGTRES-S100 system weighs ∼5.9 kg (including the UAV) and has an optimal surveying speed of 50 km/h. The system was tested along a complex coastal setting in Brittany, France, targeting mafic dykes and fault contacts with magnetite infill and magnetite nuggets (skarns). A 2.0 × 0.3 km area was mapped with a 10 m line-spacing by four sub-surveys (due to regulatory restrictions). The sub-surveys were completed in 3.5 h, including >2 h for remobilisation and the safety clearance of the area. A noise-level of ±0.02 nT was obtained and several of the key geological structures were mapped by the system.

Synchronous Issue of IR-Based Large Scale Volume Localization System

2014 ◽  
Vol 496-500 ◽  
pp. 1643-1647
Author(s):  
Ying Feng Wu ◽  
Gang Yan Li
Keyword(s):  
Standard Deviation ◽  
Mobile Robot ◽  
Large Volume ◽  
Large Scale ◽  
Image Acquisition ◽  
Mean Value ◽  
Network Synchronization ◽  
Localization System ◽  
Localization Performance ◽  
The Mean

IR-based large scale volume localization system (LSVLS) can localize the mobile robot working in large volume, which is constituted referring to the MSCMS-II. Hundreds cameras in LSVLS must be connected to the control station (PC) through network. Synchronization of cameras which are mounted on different control stations is significant, because the image acquisition of the target must be synchronous to ensure that the target is localized precisely. Software synchronization method is adopted to ensure the synchronization of camera. The mean value of standard deviation of eight cameras mounted on two workstations is 12.53ms, the localization performance of LSVLS is enhanced.

A Large-Scale Indoor Layout Reconstruction and Localization System for Spatial-Aware Mobile AR Applications

2021 ◽  
Author(s):  
Kai-Wen Hsiao ◽  
Jheng-Wei Su ◽  
Yu-Chih Hung ◽  
Kuo-Wei Chen ◽  
Chih-Yuan Yao ◽  
...  
Keyword(s):  
Large Scale ◽  
Localization System ◽  
Mobile Ar

scholarly journals Barcode based localization system in indoor environment

2014 ◽  
Vol 13 ◽  
pp. 31-36
Author(s):  
Ľubica Ilkovičová ◽  
Ján Erdélyi ◽  
Alojz Kopáčik
Keyword(s):  
Indoor Environment ◽  
Operating Principle ◽  
Quick Response ◽  
Qr Codes ◽  
Localization System ◽  
Accurate Localization ◽  
Intelligent Buildings

Nowadays, in the era of intelligent buildings, there is a need to create indoornavigation systems, what is steadily a challenge. QR (Quick Response) codesprovide accurate localization also in indoor environment, where other navigationtechniques (e.g. GPS) are not available. The paper deals with the issues of posi-tioning using QR codes, solved at the Department of Surveying, Faculty of CivilEngineering SUT in Bratislava. Operating principle of QR codes, description ofthe application for positioning in indoor environment based on OS Android forsmartphones are described.

scholarly journals Embodied intelligence via learning and evolution

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Agrim Gupta ◽  
Silvio Savarese ◽  
Surya Ganguli ◽  
Li Fei-Fei
Keyword(s):  
Intelligent Control ◽  
Energy Efficient ◽  
Large Scale ◽  
Complex Environments ◽  
Environmental Complexity ◽  
Baldwin Effect ◽  
Computational Framework ◽  
Mechanistic Basis ◽  
Embodied Intelligence ◽  
And Control

AbstractThe intertwined processes of learning and evolution in complex environmental niches have resulted in a remarkable diversity of morphological forms. Moreover, many aspects of animal intelligence are deeply embodied in these evolved morphologies. However, the principles governing relations between environmental complexity, evolved morphology, and the learnability of intelligent control, remain elusive, because performing large-scale in silico experiments on evolution and learning is challenging. Here, we introduce Deep Evolutionary Reinforcement Learning (DERL): a computational framework which can evolve diverse agent morphologies to learn challenging locomotion and manipulation tasks in complex environments. Leveraging DERL we demonstrate several relations between environmental complexity, morphological intelligence and the learnability of control. First, environmental complexity fosters the evolution of morphological intelligence as quantified by the ability of a morphology to facilitate the learning of novel tasks. Second, we demonstrate a morphological Baldwin effect i.e., in our simulations evolution rapidly selects morphologies that learn faster, thereby enabling behaviors learned late in the lifetime of early ancestors to be expressed early in the descendants lifetime. Third, we suggest a mechanistic basis for the above relationships through the evolution of morphologies that are more physically stable and energy efficient, and can therefore facilitate learning and control.

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