Tracking of trajectory with dynamic deformation based on dynamic compensation concept

Kajihara, Kenta; Huang, Shouren; Bergström, Niklas; Yamakawa, Yuji; Ishikawa, Masatoshi

doi:10.1109/robio.2017.8324709

Cited by 1 publication

(1 citation statement)

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“…However, their method requires manually identifying certain points of the target object (here a prostate) on each ultrasound frame in order to drive the model. Kajihara et al [31] developed a system that tracks the trajectory of a line drawn on a flexible object. They estimated the tracking performance with speed and precision in the presence of many uncertainties based on dynamic compensation.…”

Section: Visual Tracking Of Non-rigid Objectsmentioning

confidence: 99%

Visual Servoing on a deformable object with pre-planned trajectory-based geometric primitives

Derrar

Saidi

Abed

2023

Preprint

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This paper presents a novel approach to rigidly align a prespective camera to the deformation of a non-rigid object. We assume having a reference pre-planned camera trajectory viewing a non-rigid object. Our goal is to align this trajec-tory at the execution time given a prior only on the most relevant landmarks. Our method does not assume any parametric or non-parametric prior model on the physics of deformation. The proposed method is formalized as a tracking problem embedded in an optimal visual control framework. The tracking problem encodes visual servoing of geometric features from the deformable object between pre-planned and execution time. The optimal visual control framework is formalized with a weighted least-square criterion that minimizes the distance between the images of the reference features and the features at runtime. The weights are time-dependent smooth functions that encode the relevance of the visible object’s features. We experimentally prove that our method can adapt to multiple pre-planned trajectories and multiple types of deformations (both linear and nonlinear) without any prior knowledge on them. Robustness to noise in the detection of image features is also experimentally demonstrated.

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Section: Visual Tracking Of Non-rigid Objectsmentioning

confidence: 99%