A Flexible Software Architecture for Hybrid Tracking

Ribo, Miguel; Brandner, Markus; Pinz, Axel

doi:10.1002/rob.10124

Cited by 14 publications

(8 citation statements)

References 18 publications

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“…Also in Eino et al [15], where inertial data from an IMU is fused with the velocity estimation from a vision algorithm, no details about the scale problem are reported. Ribo et al [16] proposed an EKF to fuse vision and inertial data to estimate the 6DoF attitude. There and in [10,[17][18][19] the authors use a priori knowledge to overcome the scale problem.…”

Section: Related Workmentioning

confidence: 99%

Fusion of IMU and Vision for Absolute Scale Estimation in Monocular SLAM

Nützi¹,

Weiß²,

Scaramuzza³

et al. 2010

Unmanned Aerial Vehicles

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The fusion of inertial and visual data is widely used to improve an object's pose estimation. However, this type of fusion is rarely used to estimate further unknowns in the visual framework. In this paper we present and compare two different approaches to estimate the unknown scale parameter in a monocular SLAM framework. Directly linked to the scale is the estimation of the object's absolute velocity and position in 3D. The first approach is a spline fitting task adapted from Jung and Taylor and the second is an extended Kalman filter. Both methods have been simulated offline on arbitrary camera paths to analyze their behavior and the quality of the resulting scale estimation. We then embedded an online multi rate extended Kalman filter in the Parallel Tracking and Mapping (PTAM) algorithm of Klein and Murray together with an inertial sensor. In this inertial/monocular SLAM framework, we show a real time, robust and fast converging scale estimation. Our

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Section: Related Workmentioning

confidence: 99%

Fusion of IMU and Vision for Absolute Scale Estimation in Monocular SLAM

Nützi¹,

Weiß²,

Scaramuzza³

et al. 2010

Unmanned Aerial Vehicles

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“…Ribo et al [8,9] present a wearable AR system that is mounted on a helmet. It consists of a real-time 3D visualization subsystem (composed by a stereo see-through HMD) and a real-time tracking subsystem (composed by a camera and an IMU).…”

Section: Previous Work On Hybrid Trackingmentioning

confidence: 99%

Hybrid tracking of human operators using IMU/UWB data fusion by a Kalman filter

Ramón

Candelas

Torres

2008

Proceedings of the 3rd ACM/IEEE International Conference on Human Robot Interaction

120

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The precise localization of human operators in robotic workplaces is an important requirement to be satisfied in order to develop human-robot interaction tasks. Human tracking provides not only safety for human operators, but also context information for intelligent human-robot collaboration. This paper evaluates an inertial motion capture system which registers full-body movements of an user in a robotic manipulator workplace. However, the presence of errors in the global translational measurements returned by this system has led to the need of using another localization system, based on Ultra-WideBand (UWB) technology. A Kalman filter fusion algorithm which combines the measurements of these systems is developed. This algorithm unifies the advantages of both technologies: high data rates from the motion capture system and global translational precision from the UWB localization system. The developed hybrid system not only tracks the movements of all limbs of the user as previous motion capture systems, but is also able to position precisely the user in the environment.

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“…The 3D pose is computed from artificial landmarks [7] as depicted in figure 3(a). To avoid deficits in visual tracking of the landmark, an inertial tracker located at the top of the AR gear aids the tracking process [23]. By means of this hybrid tracking approach, the precise position and orientation of the user T pose can be computed yielding only a very small relative mean distance error of 0.5%.…”

Section: D Vision Sub-systemmentioning

confidence: 99%