Segmentation and tracking of object when grasped and moved within living spaces

Omi, Takuya; Kakusho, Koh; Iiyama, Masaaki; Nishiguchi, Shu

doi:10.1109/smc.2017.8123111

Cited by 2 publications

(3 citation statements)

References 9 publications

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“…The actual grasping process only needs to ensure that the error modulus length of the two is less than the set threshold to succeed. It is shown in Equation (26).…”

Section: Robot Position Servo Grasping Pose Planningmentioning

confidence: 99%

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A Robot Dynamic Target Grasping Method Based on Affine Group Improved Gaussian Resampling Particle Filter

Tao

Ren

et al. 2021

Applied Sciences

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Tracking and grasping a moving target is currently a challenging topic in the field of robotics. The current visual servo grasping method is still inadequate, as the real-time performance and robustness of target tracking both need to be improved. A target tracking method is proposed based on improved geometric particle filtering (IGPF). Following the geometric particle filtering (GPF) tracking framework, affine groups are proposed as state particles. Resampling is improved by incorporating an improved conventional Gaussian resampling algorithm. It addresses the problem of particle diversity loss and improves tracking performance. Additionally, the OTB2015 dataset and typical evaluation indicators in target tracking are adopted. Comparative experiments are performed using PF, GPF and the proposed IGPF algorithm. A dynamic target tracking and grasping method for the robot is proposed. It combines an improved Gaussian resampling particle filter algorithm based on affine groups and the positional visual servo control of the robot. Finally, the robot conducts simulation and experiments on capturing dynamic targets in the simulation environment and actual environment. It verifies the effectiveness of the method proposed in this paper.

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“…The actual grasping process only needs to ensure that the error modulus length of the two is less than the set threshold to succeed. It is shown in Equation (26).…”

Section: Robot Position Servo Grasping Pose Planningmentioning

confidence: 99%

“…In order to complete the grasping task, a linear prediction method was used to predict the moving object in a three-dimensional space. Takuya Omi et al [26] used the same hand to detect the grasping and release of objects. The correspondence between the objects is observed before and after the object is occluded.…”

Section: Introductionmentioning

confidence: 99%

A Robot Dynamic Target Grasping Method Based on Affine Group Improved Gaussian Resampling Particle Filter

Tao

Ren

et al. 2021

Applied Sciences

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“…Omi et al [6] propose an approach for encountering the correspondence between objects in a before and after occlusion state, by detecting the grab and release of those objects by the same hand. Due to that, segmentation of object's body is also made via the detection of the grasping and release of the object.…”

Section: Related Workmentioning

confidence: 99%

“Less is more”: Simplifying point clouds to improve grasping performance

Lopes

Alexandre

Fernandes

2018

2018 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

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Object grasping is a task that humans do without major concerns. This results from self learning and by observing of other skilled humans doing such task with previous information. However, grasping novel objects in unknown positions for a robot is a complex task which encounters many problems, such as sub-optimal performance rates and the time consumption. In this paper we present a method that complements the state-of-the-art grasping algorithms with two segmentation steps, the first one which removes the largest planar surface in the point cloud of the world before the grasp detector receives them and the second one that complements this segmentation with another segmentation that calculates where the object is located and segments the point cloud by executing a crop around the object. The proposed method significantly improves the grasping success rate (100% improvement over the baseline approach) and simultaneously is able to reduce the time consumption by 23%.

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Segmentation and tracking of object when grasped and moved within living spaces

Cited by 2 publications

References 9 publications

A Robot Dynamic Target Grasping Method Based on Affine Group Improved Gaussian Resampling Particle Filter

A Robot Dynamic Target Grasping Method Based on Affine Group Improved Gaussian Resampling Particle Filter

“Less is more”: Simplifying point clouds to improve grasping performance

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