Benchmarking pose estimation for robot manipulation

Hietanen, Antti; Latokartano, Jyrki; Foi, Alessandro; Pieters, Roel; Kyrki, Ville; Lanz, Minna; Kamarainen, Joni

doi:10.1016/j.robot.2021.103810

Cited by 7 publications

(6 citation statements)

References 23 publications

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“…However, similar to the base pose planning works, they rely on the assumption of Gaussian uncertainty. Other works connect errors in object pose and grasp success by modeling pose uncertainty using kernel regression estimated using a real physical setup [4].…”

Section: Related Work a Task Planning Assuming Pose Uncertaintymentioning

confidence: 99%

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Multi-view object pose distribution tracking for pre-grasp planning on mobile robots

Naik

Iversen

Kramberger

et al. 2022

2022 International Conference on Robotics and Automation (ICRA)

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The ability to track the 6D pose distribution of an object when a mobile manipulator robot is still approaching the object can enable the robot to pre-plan grasps that combine base and arm motion. However, tracking a 6D object pose distribution from a distance can be challenging due to the limited view of the robot camera. In this work, we present a framework that fuses observations from external stationary cameras with a moving robot camera and sequentially tracks it in time to enable 6D object pose distribution tracking from a distance. We model the object pose posterior as a multi-modal distribution which results in a better performance against uncertainties introduced by large camera-object distance, occlusions and object geometry. We evaluate the proposed framework on a simulated multi-view dataset using objects from the YCB data set. Results show that our framework enables accurate tracking even when the robot camera has poor visibility of the object.

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Section: Related Work a Task Planning Assuming Pose Uncertaintymentioning

confidence: 99%

“…[1], [2], [3]. However, in robotic tasks, a certain level of uncertainty in the object pose estimate is often tolerable [4], [5]. For instance, as shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

Multi-view object pose distribution tracking for pre-grasp planning on mobile robots

Naik

Iversen

Kramberger

et al. 2022

2022 International Conference on Robotics and Automation (ICRA)

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show abstract

“…Expectations need to be adjusted, considering the type of the materials involved, along with the properties of the robot and the gripper. The evaluation metric standing for the reliability of a certain pose estimation instance could be defined based on the conditional probability of successfully completing the task given the current pose [78].…”

Section: B Object Grasping and Manipulationmentioning

confidence: 99%

Industry 4.0 and Prospects of Circular Economy: A Survey of Robotic Assembly and Disassembly

Daneshmand¹,

Noroozi²,

Corneanu³

et al. 2021

Preprint

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Despite their contributions to the financial efficiency and environmental sustainability of industrial processes, robotic assembly and disassembly have been understudied in the existing literature. This is in contradiction to their importance in realizing the Fourth Industrial Revolution. More specifically, although most of the literature has extensively discussed how to optimally assemble or disassemble given products, the role of other factors has been overlooked. For example, the types of robots involved in implementing the sequence plans, which should ideally be taken into account throughout the whole chain consisting of design, assembly, disassembly and reassembly. Isolating the foregoing operations from the rest of the components of the relevant ecosystems may lead to erroneous inferences toward both the necessity and efficiency of the underlying procedures. In this paper we try to alleviate these shortcomings by comprehensively investigating the state-of-the-art in robotic assembly and disassembly. We consider and review various aspects of manufacturing and remanufacturing frameworks while particularly focusing on their desirability for supporting a circular economy.

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“…B Artacho et al [14] proposed OmniPose, a single-pass, endto-end trainable framework, that achieves state-of-the-art results for multi-person pose estimation. Hietanen, A et al [15] introduce an approach that connects error in pose and success in robot manipulation, and propose a probabilistic performance measure of the task success rate.…”

Section: Introductionmentioning

confidence: 99%

Lightweight Gesture Pose Estimation Based on CPM Algorithm

Wang

Yang

Wang

et al. 2022

Modern Management Based on Big Data III

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With the experiential enhancement of artificial intelligence products, gesture estimation, as a classic computer vision task, has a wide range of application scenarios. Aiming at the current network model that needs to be lightweight in mobile smart products, this paper designs a lightweight gesture pose estimation model based on the CPM (Convolutional Pose Machine) multi-stage human pose estimation network. A comparative experiment based on the RHD open-source data set was conducted to compare and analyze the lightweight CPM gesture estimation model while ensuring accuracy while effectively reducing the amount of model parameters, which provides a basis for the development of real-time mobile terminal gesture pose estimation.

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Benchmarking pose estimation for robot manipulation

Cited by 7 publications

References 23 publications

Multi-view object pose distribution tracking for pre-grasp planning on mobile robots

Multi-view object pose distribution tracking for pre-grasp planning on mobile robots

Industry 4.0 and Prospects of Circular Economy: A Survey of Robotic Assembly and Disassembly

Lightweight Gesture Pose Estimation Based on CPM Algorithm

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