Multi-sensor perceptual system for mobile robot and sensor fusion-based localization

Hoang, T. T.; Duong, P. M.; Van, Nguyen Thị Thanh; Viet, D. A.; Vinh, Trần Quang

doi:10.1109/iccais.2012.6466599

Cited by 20 publications

(13 citation statements)

References 10 publications

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“…Comparing Kalman filter to EKF, author [111] proves that that EKF algorithm is among the best method which ensures better performance and optimal result in determining robot localization. Another derivates of KF apart from EKF is Unscented Kalman filtering (UKF).…”

Section: ) Kalman Filtermentioning

confidence: 99%

A Review on Challenges of Autonomous Mobile Robot and Sensor Fusion Methods

2020

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Autonomous mobile robots are becoming more prominent in recent time because of their relevance and applications to the world today. Their ability to navigate in an environment without a need for physical or electro-mechanical guidance devices has made it more promising and useful. The use of autonomous mobile robots is emerging in different sectors such as companies, industries, hospital, institutions, agriculture and homes to improve services and daily activities. Due to technology advancement, the demand for mobile robot has increased due to the task they perform and services they render such as carrying heavy objects, monitoring, search and rescue missions, etc. Various studies have been carried out by researchers on the importance of mobile robot, its applications and challenges. This survey paper unravels the current literatures, the challenges mobile robot is being faced with. A comprehensive study on devices/sensors and prevalent sensor fusion techniques developed for tackling issues like localization, estimation and navigation in mobile robot are presented as well in which they are organised according to relevance, strengths and weaknesses. The study therefore gives good direction for further investigation on developing methods to deal with the discrepancies faced with autonomous mobile robot.

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Section: ) Kalman Filtermentioning

confidence: 99%

A Review on Challenges of Autonomous Mobile Robot and Sensor Fusion Methods

2020

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“…Such as, literature 16 presents an EKF approach to localize a mobile robot with two quadrature encoders, a compass sensor, a laser range finder (LRF), and an omni-directional camera. Literature 31 fuses the data of odometry and electronic compass using adaptive extended Kalman filter (AEKF).…”

Section: Introductionmentioning

confidence: 99%

“…So researchers are committed to combine odometry with external sensors to eliminate the odomerty errors. External sensors, such as cameras, [14][15][16][17][18][19] laser range finder, 15,16 ultrasonic, 20 radio frequency identification devices (RFID), 21 and electronic compass, 16,19 have been used to obtain the required measurements.…”

Section: Introductionmentioning

confidence: 99%

“…Because of the shortcoming of using odometry or electronic compass alone, a lot of literatures fuse the data of multisensors employing extended Kalman filter (EKF). Such as, literature 16 presents an EKF approach to localize a mobile robot with two quadrature encoders, a compass sensor, a laser range finder (LRF), and an omni-directional camera. Literature 31 fuses the data of odometry and electronic compass using adaptive extended Kalman filter (AEKF).…”

Section: Introductionmentioning

confidence: 99%

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An indoor mobile robot navigation technique using odometry and electronic compass

Chen,

Zhang

2017

International Journal of Advanced Robotic Systems

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A novel method of indoor mobile robot navigation is presented. The proposed approach fuses the data of odometry and electronic compass for navigation. It includes two calibration methods and a fusion algorithm. First of all, calibration method of systematic odometry error is used to reduce the error of navigation and provide the reliable estimate of pose of mobile robot for adaptive extended Kalman filter fusion algorithm later. Secondly, calibration method of electronic compass using an adaptive neural fuzzy inference system provides direction angle of mobile robot as observation for adaptive extended Kalman filter algorithm later. Finally, a fusion algorithm using adaptive extended Kalman filter algorithm fuses data of odometry and electronic compass that provides the position and orientation of mobile robot. In addition, the value of the parameter k of adaptive extended Kalman filter algorithm which is related to the process noise covariance is decided by fuzzy algorithm. In order to illustrate the feasibility of the proposed approach, two types of experiments are done: the first-type experiment is that the mobile robot runs along default path only with odometry, and the mobile robot with odometry and electronic compass in the second-type experiment utilizes the proposed approach for navigation. The results of experiments show that the error of localization and navigation in the first-type experiment is larger than one in the second-type experiment. They prove the good performance of the proposed approach.

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“…The laser range finder and sonar sensors in association with other sensors attached in a mobile robot. [14][15].…”

Section: Introductionmentioning

confidence: 99%

Proposal of algorithms for navigation and obstacles avoidance of autonomous mobile robot

Hoang¹,

Hiep²,

Duong³

et al. 2013

2013 IEEE 8th Conference on Industrial Electronics and Applications (ICIEA)

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Abstract-This paper presents algorithms to navigate and avoid obstacles for an in-door autonomous mobile robot. A laser range finder is used to obtain 3D images of the environment. A new algorithm, namely 3D-to-2D image pressure and barriers detection (IPaBD), is proposed to create a 2D global map from the 3D images. This map is basic to design the trajectory. A tracking controller is developed to control the robot to follow the trajectory. The obstacle avoidance is addressed with the use of sonar sensors. An improved vector field histogram (Improved-VFH) algorithm is presented with improvements to overcome some limitations of the original VFH. Experiments have been conducted and the result is encouraged.

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Multi-sensor perceptual system for mobile robot and sensor fusion-based localization

Cited by 20 publications

References 10 publications

A Review on Challenges of Autonomous Mobile Robot and Sensor Fusion Methods

A Review on Challenges of Autonomous Mobile Robot and Sensor Fusion Methods

An indoor mobile robot navigation technique using odometry and electronic compass

Proposal of algorithms for navigation and obstacles avoidance of autonomous mobile robot

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