Sensor data fusion using Unscented Kalman Filter for accurate localization of mobile robots

Anjum, Muhammad Latif; Park, Jaehong; Hwang, Wonsang; Kwon, Hyun-il; Kim, Jonghyeon; Lee, Chang-Hun; Kim, Kwangsoo; Cho, Dong-il Danr

doi:10.1109/iccas.2010.5669779

Cited by 18 publications

(8 citation statements)

References 10 publications

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“… Simultaneous Localization and Mapping (SLAM) [56].  Unscented Kalman Filter [43].  Boiler Header Inspection Robot (BHIR) [58].…”

Section: Inspectionmentioning

confidence: 99%

“… Simultaneous Localization and Mapping (SLAM) [56].  Unscented Kalman Filter [43].  Inspection of Circumferential Pipe Parts [44], [145].…”

Section: Image Analysis 77%mentioning

confidence: 99%

“…A Kalman filter is implemented to the laser range finder readings to reduce the noises and errors. Anjum et al [43] presented a method that utilized a low-cost accelerometer, gyroscope, and encoders for robot localization and slips error compensation. An unscented Kalman Filter is applied as an efficient sensor fusion technique.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Localization, Mapping, Navigation, and Inspection Methods in In-Pipe Robots: A Review

Kazeminasab¹,

Sadeghi

Janfaza

et al. 2021

IEEE Access

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“… Simultaneous Localization and Mapping (SLAM) [56].  Unscented Kalman Filter [43].  Boiler Header Inspection Robot (BHIR) [58].…”

Section: Inspectionmentioning

confidence: 99%

“… Simultaneous Localization and Mapping (SLAM) [56].  Unscented Kalman Filter [43].  Inspection of Circumferential Pipe Parts [44], [145].…”

Section: Image Analysis 77%mentioning

confidence: 99%

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Localization, Mapping, Navigation, and Inspection Methods in In-Pipe Robots: A Review

Kazeminasab¹,

Sadeghi

Janfaza

et al. 2021

IEEE Access

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“…This filter was first used to solve trajectory tracking problems in preparation of the Apollo flights. It has become an essential algorithm for the trajectory processing in modern radar systems and other engineering applications [18][19][20][21][22][23][24][25][26][27]. In this paper, the application of KF plays an indispensable role, which aims to reduce the measurement noise of the used sensors in the proposed prototype, and through it, we can stabilize the operation of system in order to make it more efficient.…”

Section: Kalman Filter Algorithmmentioning

confidence: 99%

Embedded System Prototype to Fight Covid-19 Pandemic Contamination with Less Cost

Adardour

Hadjila

Irid

et al. 2021

Wireless Pers Commun

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In the shadow of the coronavirus (Covid-19) pandemic, the sterilization has become a major necessity for humans to avoid exponential Infection. In order to avoid touching contaminated surfaces, we develop an embedded system prototype, which allows the bottle or any other medium to pour the sterilization product without touching it. The human hands are detected using two sensors HC-SR04 and LM35. To overcome the stability problem due to the problem of sensor noise and enhance the system performance a Kalman filter algorithm is implemented to ensure stable hands detection. The efficiency of the prototype mounted on an Arduino board is checked. After the completion of the prototype, a comprehensive cost analysis is conducted.

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“…However, the EKF based fusion algorithms involve local linearization of the system. In order to explicitly account for the nonlinearities, involved in the dynamical model, few progressive approaches consider the Unscented Kalman Filter (UKF) [12,13] and the Particle Filter (PF) [14] based multi-sensor fusion for robotic localization. Even though the UKF and PF are superior for addressing nonlinearities, the advantage comes with additional complexity in the computational burden.…”

Section: Introductionmentioning

confidence: 99%

A Decentralized Sensor Fusion Scheme for Multi Sensorial Fault Resilient Pose Estimation

Mukherjee

Banerjee

Papadimitriou

et al. 2021

Sensors

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This article proposes a novel decentralized two-layered and multi-sensorial based fusion architecture for establishing a novel resilient pose estimation scheme. As it will be presented, the first layer of the fusion architecture considers a set of distributed nodes. All the possible combinations of pose information, appearing from different sensors, are integrated to acquire various possibilities of estimated pose obtained by involving multiple extended Kalman filters. Based on the estimated poses, obtained from the first layer, a Fault Resilient Optimal Information Fusion (FR-OIF) paradigm is introduced in the second layer to provide a trusted pose estimation. The second layer incorporates the output of each node (constructed in the first layer) in a weighted linear combination form, while explicitly accounting for the maximum likelihood fusion criterion. Moreover, in the case of inaccurate measurements, the proposed FR-OIF formulation enables a self resiliency by embedding a built-in fault isolation mechanism. Additionally, the FR-OIF scheme is also able to address accurate localization in the presence of sensor failures or erroneous measurements. To demonstrate the effectiveness of the proposed fusion architecture, extensive experimental studies have been conducted with a micro aerial vehicle, equipped with various onboard pose sensors, such as a 3D lidar, a real-sense camera, an ultra wide band node, and an IMU. The efficiency of the proposed novel framework is extensively evaluated through multiple experimental results, while its superiority is also demonstrated through a comparison with the classical multi-sensorial centralized fusion approach.

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Sensor data fusion using Unscented Kalman Filter for accurate localization of mobile robots

Cited by 18 publications

References 10 publications

Localization, Mapping, Navigation, and Inspection Methods in In-Pipe Robots: A Review

Localization, Mapping, Navigation, and Inspection Methods in In-Pipe Robots: A Review

Embedded System Prototype to Fight Covid-19 Pandemic Contamination with Less Cost

A Decentralized Sensor Fusion Scheme for Multi Sensorial Fault Resilient Pose Estimation

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