The localization of a mobile robot using a pseudolite ultrasonic system and a dead reckoning integrated system

Kim, Su Yong; Yoon, Kang Sup; Lee, Dong Hwal; Lee, Man Hyung

doi:10.1007/s12555-011-0216-1

Cited by 20 publications

(4 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The quaternion update values are expressed by (14). The gyroscopic heading is obtained by following equation.…”

Section: Heading Estimationmentioning

confidence: 99%

“…Currently, different indoor positioning systems have been developed. These include wireless local area network (WLAN) [6], ultrawideband (UWB) communication [7], [8], Wi-Fi-based systems [9]- [11], camera based systems [12], [13], ultrasonic sensors [14], [15], laser-based systems [16], radio frequency identification (RFID) [17]- [20], inertial navigation and inertial measurement unit (IMU)-based navigation systems [21], [22], global system for mobile communications (GSM) [23]- [26], and smartphone-based positiondetection systems [27]- [30]. Among these techniques, the smartphone sensor based position-detection system is the most popular.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Indoor Position-Estimation Algorithm Using Smartphone IMU Sensor Data

2019

View full text Add to dashboard Cite

Position-estimation systems for indoor localization play an important role in everyday life. The global positioning system (GPS) is a popular positioning system, which is mainly efficient for outdoor environments. In indoor scenarios, GPS signal reception is weak. Therefore, achieving good position estimation accuracy is a challenge. To overcome this challenge, it is necessary to utilize other positionestimation systems for indoor localization. However, other existing indoor localization systems, especially based on inertial measurement unit (IMU) sensor data, still face challenges such as accumulated errors from sensors and external magnetic field effects. This paper proposes a position-estimation algorithm that uses the combined features of the accelerometer, magnetometer, and gyroscope data from an IMU sensor for position estimation. In this paper, we first estimate the pitch and roll values based on a fusion of accelerometer and gyroscope sensor values. The estimated pitch values are used for step detection. The step lengths are estimated by using the pitching amplitude. The heading of the pedestrian is estimated by the fusion of magnetometer and gyroscope sensor values. Finally, the position is estimated based on the step length and heading information. The proposed pitch-based step detection algorithm achieves 2.5% error as compared with acceleration-based step detection approaches. The heading estimation proposed in this paper achieves a mean heading error of 4.72 • as compared with the azimuth-and magnetometer-based approaches. The experimental results show that the proposed position-estimation algorithm achieves a high position accuracy that significantly outperforms that of conventional estimation methods used for validation in this paper. INDEX TERMS Indoor positioning system (IPS), pedestrian dead reckoning (PDR), heading estimation, indoor navigation, Android-based smartphone, quaternion, Kalman filter, sensor fusion.

show abstract

“…The quaternion update values are expressed by (14). The gyroscopic heading is obtained by following equation.…”

Section: Heading Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Indoor Position-Estimation Algorithm Using Smartphone IMU Sensor Data

2019

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

The localization of a mobile robot using a pseudolite ultrasonic system and a dead reckoning integrated system

Cited by 20 publications

References 13 publications

An Indoor Position-Estimation Algorithm Using Smartphone IMU Sensor Data

An Indoor Position-Estimation Algorithm Using Smartphone IMU Sensor Data

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

Contact Info

Product

Resources

About