Distributed robots localization and cooperative target tracking

Rasoulidanesh, Maryam S.; Mohsin, Nasreen; Payandeh, Shahram

doi:10.1109/iemcon.2017.8117204

Cited by 2 publications

(3 citation statements)

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“…Earlier, the patterned planar surfaces such as alternating black and white squares or circles was used to calibrate sensors which acquires RGB data. 5 Ref. [18] incorporates similar approach to calibrate depth data-based sensors with a clear and distinguishable pattern as shown in Fig.…”

Section: D1 Multiple Camera Calibration Parameters Using Patterned Planar Surfacesmentioning

confidence: 99%

“…An amalgamation of information from the co-located RGB and depth sensors can be used in order to segment and map 3D point cloud in order to determine body pose, 2 tracking head and face, 3 hand gestures recognition 4 and service robot navigation planning and tracking. 5 In order to carry out such tasks, one of the most important preliminary steps is the camera calibration.…”

Section: Introductionmentioning

confidence: 99%

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Calibration of Multiple Depth Sensor Network Using Reflective Pattern on Spheres: Theory and Experiments

Mohsin

Payandeh

2020

Robotica

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SUMMARY The usage of depth data from time-of-flight or any equivalent devices overcomes visual challenges presented under low illumination. For integrating such data from multiple sources, the paper proposes a novel tool for the calibration of sensors. The proposed tool consists of retro-reflective stripped spheres. To correctly estimate these spheres, the paper investigates the performance of spherical estimations from either infrared or depth data. The relationship between sensors is determined by calculating the poses between the calibration tool and each sensor. The paper evaluates and compares the proposed approach against other state-of-the-art approaches in terms of shape reconstruction and spatial consistency.

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Section: D1 Multiple Camera Calibration Parameters Using Patterned Planar Surfacesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Calibration of Multiple Depth Sensor Network Using Reflective Pattern on Spheres: Theory and Experiments

Mohsin

Payandeh

2020

Robotica

Self Cite

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“…Classical tracking methods have been extensively studied and considered as a divide-and-conquer strategy: first calculate the measurement-to-target association, and then use filtering technologies to estimate the states [12]- [14]. Under target tracking framework, robust association function is needed [15], [16]. However, it is a chicken-and-egg problem: in density cluttered scenarios, calculating correct association is quite challenging.…”

Section: Introductionmentioning

confidence: 99%

Multiple Vessel Cooperative Localization Under Random Finite Set Framework With Unknown Birth Intensities

Zhang

et al. 2020

IEEE Access

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The key challenge for multiple vessel cooperative localization is considered as data association, in which state-of-the-art approaches adopt a divide-and-conquer strategy to acquire measurement-to-target association. However, traditional approaches suffer both the computational time and accuracy issues. Here, an improved algorithm under Random Finite Set statistics (RFSs) is proposed, in which the Probability Hypothesis Density (PHD) filter is utilized to address the aforementioned issues, by jointly estimating both the number of vessels and the corresponding states in complex environments. Furthermore, to avoid the prior requirement constrain with respect to the PHD filter, the pattern recognition method is simultaneously utilized to calculate the birth intensities. Simulation results exhibit the proposed approach performs better than normal PHD for multiple vessel cooperative localization, in scenarios of unknown birth intensity. INDEX TERMS Cooperative localization, point matching, probability hypothesis density (PHD) filter.

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Distributed robots localization and cooperative target tracking

Cited by 2 publications

References 21 publications

Calibration of Multiple Depth Sensor Network Using Reflective Pattern on Spheres: Theory and Experiments

Calibration of Multiple Depth Sensor Network Using Reflective Pattern on Spheres: Theory and Experiments

Multiple Vessel Cooperative Localization Under Random Finite Set Framework With Unknown Birth Intensities

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