Localization of a Car Based on Multi-Sensor Fusion

Kim, H.; Lee, I.

doi:10.5194/isprs-archives-xlii-1-247-2018

Cited by 13 publications

(5 citation statements)

References 10 publications

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“…Ref. [77] suggested a localization algorithm by combining a camera, GPS, and onboard sensors for precise vehicle placement. They combined GPS data with ocular odometry using the extended Kalman filtering algorithm, increasing the accuracy above conventional GPS positioning techniques by 40%.…”

Section: Fusion Strategy Based On Multi-source Decision (Fsbmd)mentioning

confidence: 99%

Analyzing Factors Influencing Situation Awareness in Autonomous Vehicles—A Survey

Ignatious

El-Sayed

Khan

et al. 2023

Sensors

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Autonomous driving of higher automation levels asks for optimal execution of critical maneuvers in all environments. A crucial prerequisite for such optimal decision-making instances is accurate situation awareness of automated and connected vehicles. For this, vehicles rely on the sensory data captured from onboard sensors and information collected through V2X communication. The classical onboard sensors exhibit different capabilities and hence a heterogeneous set of sensors is required to create better situation awareness. Fusion of the sensory data from such a set of heterogeneous sensors poses critical challenges when it comes to creating an accurate environment context for effective decision-making in AVs. Hence this exclusive survey analyses the influence of mandatory factors like data pre-processing preferably data fusion along with situation awareness toward effective decision-making in the AVs. A wide range of recent and related articles are analyzed from various perceptive, to pick the major hiccups, which can be further addressed to focus on the goals of higher automation levels. A section of the solution sketch is provided that directs the readers to the potential research directions for achieving accurate contextual awareness. To the best of our knowledge, this survey is uniquely positioned for its scope, taxonomy, and future directions.

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Section: Fusion Strategy Based On Multi-source Decision (Fsbmd)mentioning

confidence: 99%

Analyzing Factors Influencing Situation Awareness in Autonomous Vehicles—A Survey

Ignatious

El-Sayed

Khan

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…In addition, another algorithm based on multi-sensor data fusion was proposed in [131]. The sensors in the data collection stage include GPS and camera, and the geographic information databases are employed to reduce the impact of accumulated errors on the accuracy due to time change.…”

Section: B Data Fusion-based Multi-sensor Localizationmentioning

confidence: 99%

A Survey of Vehicle Localization: Performance Analysis and Challenges

Shan,

Cabani,

Chafouk

2023

IEEE Access

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Vehicle localization plays a crucial role in ensuring the safe operation of autonomous vehicles and the development of intelligent transportation systems (ITS). However, there is insufficient effort to compare the performance and challenges of different vehicle localization algorithms. This paper aims to address this gap by analyzing the comprehensive performance of existing advanced vehicle localization techniques and discussing their challenges. Firstly, we analyze the self-localization methods based on active and passive sensors. The results show that, the light detection and ranging (LiDAR) and vision-based techniques can reach high accuracy. However, they have high computational complexity. And only using the inertial measurement unit (IMU), global positioning system (GPS), radar, and ultrasonic sensors may not realize localization result with high accuracy. Then, we discuss V2X-based cooperative methods and analyze the multi-sensor based localization techniques and compare the comprehensive performance among all methods. Although the artificial intelligence (AI) techniques can effectively enhance the efficiency of vision-based localization algorithms, the high computational complexity still should be considered. In addition, since the IMU, GPS, radar, and ultrasonic sensors have good performance in terms of the availability, scalability, computational complexity, and cost-effectiveness, they can be used as auxiliary sensors to achieve good comprehensive performance through data fusion techniques. Finally, we propose the challenges of different techniques and look forward to future work.

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“…This EKF is based on error states. Kim and Lee [ 18 ]proposed the EKF algorithm. This algorithm is a combination of a Camera, GPS, and sensor of in-vehicle for the precise positioning of vehicles.…”

Section: Related Workmentioning

confidence: 99%

A task level fusion autonomous switching mechanism

Lv,

Wang,

Zhang

2023

PLoS ONE

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Positioning technology is an important component of environmental perception. It is also the basis for autonomous decision-making and motion control of firefighting robots. However, some issues such as positioning in indoor scenarios still remain inherent challenges. The positioning accuracy of the fire emergency reaction dispatching (FERD) system is far from adequate to support some applications for firefighting and rescue in indoor scenarios with multiple obstacles. To solve this problem, this paper proposes a fusion module based on the Blackboard architecture. This module aims to improve the positioning accuracy of a single sensor of the unmanned vehicles within the FERD system. To reduce the risk of autonomous decision-making of the unmanned vehicles, this module uses a comprehensive manner of multiple channels to complement or correct the positioning of the firefighting robots. Specifically, this module has been developed to fusion a variety of relevant processes for precise positioning. This process mainly includes six strategies. These strategies are the denoising, spatial alignment, confidence degree update, observation filtering, data fusion, and fusion decision. These strategies merge with the current scenarios-related parameter data, empirical data on sensor errors, and information to form a series of norms. This paper then proceeds to gain experience data with the confidence degree, error of different sensors, and timeliness of this module by training in an indoor scenario with multiple obstacles. This process is from data of multiple sensors (bottom-level) to control decisions knowledge-based (up-level). This process can obtain globally optimal positioning results. Finally, this paper evaluates the performance of this fusion module for the FERD system. The experimental results show that this fusion module can effectively improve positioning accuracy in an indoor scenario with multiple obstacles. Code is available at https://github.com/lvbingyu-zeze/gopath/tree/master.

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Localization of a Car Based on Multi-Sensor Fusion

Cited by 13 publications

References 10 publications

Analyzing Factors Influencing Situation Awareness in Autonomous Vehicles—A Survey

Analyzing Factors Influencing Situation Awareness in Autonomous Vehicles—A Survey

A Survey of Vehicle Localization: Performance Analysis and Challenges

A task level fusion autonomous switching mechanism

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