Pose estimation using monocular vision and inertial sensors aided with ultra wide band

Nyqvist, Hanna; Skoglund, Martin A.; Hendeby, Gustaf; Gustafsson, Fredrik

doi:10.1109/ipin.2015.7346940

Cited by 25 publications

(15 citation statements)

References 32 publications

(35 reference statements)

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“…Results achieved by the optical positioning systems are quite interesting; however relatively high computation power is needed for image processing algorithms to estimate position and implementation of optical tags is commonly required in the area of positioning [14,15]. Implementation of optical tags may be either problematic or even impossible in some areas.…”

Section: Introductionmentioning

confidence: 99%

Scalability Optimization of Seamless Positioning Service

Machaj¹,

Brída²,

Benikovsky³

2016

Mobile Information Systems

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Recently positioning services are getting more attention not only within research community but also from service providers. From the service providers point of view positioning service that will be able to work seamlessly in all environments, for example, indoor, dense urban, and rural, has a huge potential to open new markets. However, such system does not only need to provide accurate position estimates but have to be scalable and resistant to fake positioning requests. In the previous works we have proposed a modular system, which is able to provide seamless positioning in various environments. The system automatically selects optimal positioning module based on available radio signals. The system currently consists of three positioning modules—GPS, GSM based positioning, and Wi-Fi based positioning. In this paper we will propose algorithm which will reduce time needed for position estimation and thus allow higher scalability of the modular system and thus allow providing positioning services to higher amount of users. Such improvement is extremely important, for real world application where large number of users will require position estimates, since positioning error is affected by response time of the positioning server.

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Section: Introductionmentioning

confidence: 99%

Scalability Optimization of Seamless Positioning Service

Machaj¹,

Brída²,

Benikovsky³

2016

Mobile Information Systems

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“…Through the IMU integral data, the observations of speed, direction, and position can be obtained. To a certain extent, it can not only eliminate the CKF has three advantages [20,30,31]: first, there is no need to define a motion model, that is, the algorithm can be used for both vehicle and pedestrian applications. Secondly, the error of the state is stored rather than the state itself.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive UWB/MEMS-IMU Complementary Kalman Filter for Indoor Location in NLOS Environment

Liu

Jian

et al. 2019

Remote Sensing

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High precision positioning of UWB (ultra-wideband) in NLOS (non-line-of-sight) environment is one of the hot issues in the direction of indoor positioning. In this paper, a method of using a complementary Kalman filter (CKF) to fuse and filter UWB and IMU (inertial measurement unit) data and track the errors of variables such as position, speed, and direction is presented. Based on the uncertainty of magnetometer and acceleration, the noise covariance matrix of magnetometer and accelerometer is calculated dynamically, and then the weight of magnetometer data is set adaptively to correct the directional error of gyroscope. Based on the uncertainty of UWB distance observations, the covariance matrix of UWB measurement noise is calculated dynamically, and then the weight of UWB data observations is set adaptively to correct the position error. The position, velocity and direction errors are corrected by the fusion of UWB and IMU. The experimental results show that the algorithm can reduce the gyroscope deviation with magnetic noise and motion noise, so that the orientation estimates can be improved, as well as the positioning accuracy can be increased with UWB ranging noise.

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“…It was the first time that the positioning of a flying drone with the integration of vision, IMU, and UWB was proposed to realize the twodimensional positioning accuracy of 10 cm. However, in the literature [23], visual-inertial SLAM (simultaneous localization and mapping) technology was used for the positioning of a flying drone. Meanwhile, the adoption of UWB technology for error correction had obtained a full sixDoF pose of the drone.…”

Section: Introductionmentioning

confidence: 99%

Research on Extended Kalman Filter and Particle Filter Combinational Algorithm in UWB and Foot-Mounted IMU Fusion Positioning

Wang

Liu

2018

Mobile Information Systems

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As UWB high-precision positioning in NLOS environment has become one of the hot topics in the research of indoor positioning, this paper firstly presents a method for the smoothing of original range data based on the Kalman filter by the analysis of the range error caused by UWB signals in LOS and NLOS environment. en, it studies a UWB and foot-mounted IMU fusion positioning method with the integration of particle filter with extended Kalman filter. is method adopts EKF algorithm in the kinematic equation of particle filters algorithm to calculate the position of each particle, which is like the way of running N (number of particles) extended Kalman filters, and overcomes the disadvantages of the inconformity between kinematic equation and observation equation as well as the problem of sample degeneration under the nonlinear condition of the standard particle filters algorithm. e comparison with the foot-mounted IMU positioning algorithm, the optimization-based UWB positioning algorithm, the particle filter-based UWB positioning algorithm, and the particle filter-based IMU/UWB fusion positioning algorithm shows that our algorithm works very well in LOS and NLOS environment. Especially in an NLOS environment, our algorithm can better use the foot-mounted IMU positioning trajectory maintained by every particle to weaken the influence of range error caused by signal blockage. It outperforms the other four algorithms described as above in terms of the average and maximum positioning error.

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Pose estimation using monocular vision and inertial sensors aided with ultra wide band

Cited by 25 publications

References 32 publications

Scalability Optimization of Seamless Positioning Service

Scalability Optimization of Seamless Positioning Service

An Adaptive UWB/MEMS-IMU Complementary Kalman Filter for Indoor Location in NLOS Environment

Research on Extended Kalman Filter and Particle Filter Combinational Algorithm in UWB and Foot-Mounted IMU Fusion Positioning

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