Hazardous area navigation with temporary beacons

Eom, Wesub; Park, Jaehyun; Lee, Jang-Myung

doi:10.1007/s12555-010-0517-9

Cited by 11 publications

(3 citation statements)

References 13 publications

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“…In [2], the authors classified two groups of sensors based on measurement techniques used: relative positioning sensors such as wheel odometry [1], visual odometry [3] and inertial sensor [4]. On the other hand, absolute positioning sensors such as Global Positioning Systems [5], [6], landmark navigation [7], [8], active beacons [9], ultrasound range sensor [10], light detection and ranging (LiDAR) [11], cameras [12] and magnetic compasses [1]. The two groups have pros and cons.…”

Section: Introductionmentioning

confidence: 99%

Low-Cost Reduced Navigation System for Mobile Robot in Indoor/Outdoor Environments

2020

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This paper presents a low-cost based approach for solving the navigation problem of wheeled mobile robots to perform required tasks within indoor and outdoor environments. The presented solution is based on probabilistic approaches for multiple sensor fusion utilizing low-cost visual/inertial sensors. For the outdoor environment, the Extended Kalman Filter (EKF) is used to estimate the robot position and orientation, the system consists of wheel encoders, a reduced inertial sensor system (RISS), and a Global Positioning System (GPS). For the indoor environment, where GPS signals are blocked, another EKF algorithm is proposed using low cost depth sensor (Microsoft Kinect stream). EKF indoor localization is based on landmarks extracted from the depth measurements. A hybrid low-cost reduced navigation system (HLRNS) for indoor and outdoor environments is proposed and validated in both simulation and experimental environments. Additionally, an input-output state feedback linearization (I-O SFL) technique is used to control the robot to track the desired trajectory in such an environment. According to the conducted validation simulation and experimental testing, the proposed HLRNS provides an acceptable performance to be deployed for real-time applications. INDEX TERMS Extended kalman filter, Kinect depth sensor, low-cost navigation, mobile robot, sensor fusion.

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Section: Introductionmentioning

confidence: 99%

Low-Cost Reduced Navigation System for Mobile Robot in Indoor/Outdoor Environments

2020

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“…Therefore, it is necessary to explore the new positioning methods with better quality. For now, various techniques have been developed, such as Ultra-WideBand (UWB) communication [ 3 ], wireless local area network (WLAN) [ 4 , 5 ], WiFi-based [ 6 , 7 , 8 ], camera-based [ 9 , 10 ], ultrasonic sensors [ 11 , 12 ], laser-based [ 13 ], radio frequency identification (RFID) [ 14 , 15 ] and strapdown inertial navigation [ 16 , 17 , 18 ]. Among these techniques, inertial measurement unit (IMU)-based navigation is more competitive, owing to its independence of the existing infrastructures in buildings.…”

Section: Introductionmentioning

confidence: 99%

Quaternion-Based Unscented Kalman Filter for Accurate Indoor Heading Estimation Using Wearable Multi-Sensor System

Yuan

Zhang

et al. 2015

Sensors

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Inertial navigation based on micro-electromechanical system (MEMS) inertial measurement units (IMUs) has attracted numerous researchers due to its high reliability and independence. The heading estimation, as one of the most important parts of inertial navigation, has been a research focus in this field. Heading estimation using magnetometers is perturbed by magnetic disturbances, such as indoor concrete structures and electronic equipment. The MEMS gyroscope is also used for heading estimation. However, the accuracy of gyroscope is unreliable with time. In this paper, a wearable multi-sensor system has been designed to obtain the high-accuracy indoor heading estimation, according to a quaternion-based unscented Kalman filter (UKF) algorithm. The proposed multi-sensor system including one three-axis accelerometer, three single-axis gyroscopes, one three-axis magnetometer and one microprocessor minimizes the size and cost. The wearable multi-sensor system was fixed on waist of pedestrian and the quadrotor unmanned aerial vehicle (UAV) for heading estimation experiments in our college building. The results show that the mean heading estimation errors are less 10° and 5° to multi-sensor system fixed on waist of pedestrian and the quadrotor UAV, respectively, compared to the reference path.

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“…A pedestrian navigation system provides a person’s location indoors or outdoors. Many different technologies are used for the pedestrian navigation such as vision [ 1 ], wireless technology [ 2 ], ultrasonic sensors [ 3 , 4 ], and inertial sensors [ 5 – 10 ]. Among them, an inertial sensor-based pedestrian navigation system computes the location using inertial sensors installed on a shoe [ 5 – 10 ].…”

Section: Introductionmentioning

confidence: 99%

Height Compensation Using Ground Inclination Estimation in Inertial Sensor-Based Pedestrian Navigation

Park

Suh

2011

Sensors

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In an inertial sensor-based pedestrian navigation system, the position is estimated by double integrating external acceleration. A new algorithm is proposed to reduce z axis position (height) error. When a foot is on the ground, a foot angle is estimated using accelerometer output. Using a foot angle, the inclination angle of a road is estimated. Using this road inclination angle, height difference of one walking step is estimated and this estimation is used to reduce height error. Through walking experiments on roads with different inclination angles, the usefulness of the proposed algorithm is verified.

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Hazardous area navigation with temporary beacons

Cited by 11 publications

References 13 publications

Low-Cost Reduced Navigation System for Mobile Robot in Indoor/Outdoor Environments

Low-Cost Reduced Navigation System for Mobile Robot in Indoor/Outdoor Environments

Quaternion-Based Unscented Kalman Filter for Accurate Indoor Heading Estimation Using Wearable Multi-Sensor System

Height Compensation Using Ground Inclination Estimation in Inertial Sensor-Based Pedestrian Navigation

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