Direct visual servoing using network-synchronized cameras and Kalman filter

Schuurman, Derek C.; Capson, David W.

doi:10.1109/robot.2002.1014409

Cited by 6 publications

(3 citation statements)

References 12 publications

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“…The main judges of edge detection are first directive and second directive of image. But directive calculation is sensitive to noise, and this is the reason why the image needs to be filtered when detecting edge [7]. Most of the filtering algorithm reduces edge strength as filtering noise.…”

Section: Control Strategymentioning

confidence: 99%

Autonomous Mobile Robot for Small Advertisements Cleaning

Xiao

Wang

2011

AMR

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A mobile robot with autonomous recognition and automatic clearing small advertisements on the ground is presented. It’s the service-oriented robot for cleaning of small advertisements. The robot body is designed according to the design requirements of clearing and the control strategies for tracing and recognition small advertisements are proposed. The key issue of the robot is the hardware design for moving clean assisted by vision system and corresponding recognition methods. The results show the feasibility of the hardware structure, circuit design and program algorithm.

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Section: Control Strategymentioning

confidence: 99%

Autonomous Mobile Robot for Small Advertisements Cleaning

Xiao

Wang

2011

AMR

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“…In [14], [15], and [16], the multiple cameras are dealt with as a single generalized camera. Multiple cameras are used in [17] and in [18] mainly as fixed cameras to estimate the pose of an object with a known CAD model. To sum up, our motivation is to make use of the parallel processing to estimate the pose of a moving robot within an unknown indoor scene in real time.…”

Section: Introductionmentioning

confidence: 99%

A Parallel Implementation of Multiple Non-overlapping Cameras for Robot Pose Estimation

2014

KSII TIIS

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Image processing and computer vision algorithms are gaining larger concern in a variety of application areas such as robotics and man-machine interaction. Vision allows the development of flexible, intelligent, and less intrusive approaches than most of the other sensor systems. In this work, we determine the location and orientation of a mobile robot which is crucial for performing its tasks. In order to be able to operate in real time there is a need to speed up different vision routines. Therefore, we present and evaluate a method for introducing parallelism into the multiple non-overlapping camera pose estimation algorithm proposed in [1]. In this algorithm the problem has been solved in real time using multiple non-overlapping cameras and the Extended Kalman Filter (EKF). Four cameras arranged in two back-to-back pairs are put on the platform of a moving robot. An important benefit of using multiple cameras for robot pose estimation is the capability of resolving vision uncertainties such as the bas-relief ambiguity. The proposed method is based on algorithmic skeletons for low, medium and high levels of parallelization. The analysis shows that the use of a multiprocessor system enhances the system performance by about 87%. In addition, the proposed design is scalable, which is necaccery in this application where the number of features changes repeatedly.

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“…For example, [4] uses neural networks to model the transition from the visual domain to the joint domain and a self-learning structure in the joint space is employed for control. Schuurman and Capson [5] apply a Kalman Filter to estimate robot position. Fugimoto and Hori [6] use a multirate controller for disturbance rejection and an inter-sample observer to increase overall system robustness.…”

Section: Introductionmentioning

confidence: 99%