Real-time pose estimation and tracking of rigid objects in 3D space using extended Kalman filter

Hajimolahoseini, Habib; Amirfattahi, Rassoul; Khorshidi, Shapoor

doi:10.1109/iraniancee.2014.6999781

Cited by 1 publication

(1 citation statement)

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“…Most of the related works reported for pose estimation are based on features-based method in which man-made features are extracted from images and matched the corresponding features in other images [3,10]. There are some notable studies on 3D pose estimation and tracking [25][26][27][28][29]. In 3D pose estimation, interpretation such as epipolar geometry using multiple views provides precise pose estimation, especially camera depth measurement.…”

Section: Problem Statementmentioning

confidence: 99%

Visual Servoing for Underwater Vehicle Using Dual-Eyes Evolutionary Real-Time Pose Tracking

Myint

Yonemori

Yanou³

et al. 2016

J. Robot. Mechatron.

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[abstFig src='/00280004/12.jpg' width='300' text='ROV with dual-eyes cameras and 3D marker' ] Recently, a number of researches related to underwater vehicle has been conducted worldwide with the huge demand in different applications. In this paper, we propose visual servoing for underwater vehicle using dual-eyes cameras. A new method of pose estimation scheme that is based on 3D model-based recognition is proposed for real-time pose tracking to be applied in Autonomous Underwater Vehicle (AUV). In this method, we use 3D marker as a passive target that is simple but enough rich of information. 1-step Genetic Algorithm (GA) is utilized in searching process of pose in term of optimization, because of its effectiveness, simplicity and promising performance of recursive evaluation, for real-time pose tracking performance. The proposed system is implemented as software implementation and Remotely Operated Vehicle (ROV) is used as a test-bed. In simulated experiment, the ROV recognizes the target, estimates the relative pose of vehicle with respect to the target and controls the vehicle to be regulated in desired pose. PID control concept is adapted for proper regulation function. Finally, the robustness of the proposed system is verified in the case when there is physical disturbance and in the case when the target object is partially occluded. Experiments are conducted in indoor pool. Experimental results show recognition accuracy and regulating performance with errors kept in centimeter level.

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