In Situ Translational Hand-Eye Calibration of Laser Profile Sensors using Arbitrary Objects

Murali, Prajval Kumar; Sorrentino, Ines; Rendiniello, Angelo; Fantacci, Claudio; Villagrossi, Enrico; Polo, Andrea de; Ardesi, Alessandro; Maggiali, Marco; Natale, Lorenzo; Pucci, Daniele; Traversaro, Silvio

doi:10.1109/icra48506.2021.9561055

Cited by 5 publications

(1 citation statement)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The tactile point cloud is expressed in the common world coordinate frame W using the robot's kinematic model. An Azure Kinect DK RGB-D camera is rigidly attached to the Panda Gripper with a custom designed flange which provides the vision point cloud v S and is expressed in the world-frame using hand-eye calibration [45]. Both visual and tactile point clouds are only composed of the x, y, z coordinates and other properties such as normals, colour are not used.…”

Section: A Experimental Setup and Data Collectionmentioning

confidence: 99%

Deep Active Cross-Modal Visuo-Tactile Transfer Learning for Robotic Object Recognition

Murali

Wang

Lee

et al. 2022

IEEE Robot. Autom. Lett.

Self Cite

View full text Add to dashboard Cite

We propose for the first time, a novel deep active visuo-tactile cross-modal full-fledged framework for object recognition by autonomous robotic systems. Our proposed network xAVTNet is actively trained with labelled point clouds from a vision sensor with one robot and tested with an active tactile perception strategy to recognise objects never touched before using another robot. We propose a novel visuo-tactile loss (VTLoss) to minimise the discrepancy between the visual and tactile domains for unsupervised domain adaptation. Our framework leverages the strengths of deep neural networks for cross-modal recognition along with active perception and active learning strategies for increased efficiency by minimising redundant data collection. Our method is extensively evaluated on a real robotic system and compared against baselines and other state-of-art approaches. We demonstrate clear outperformance in recognition accuracy compared to the state-of-art visuo-tactile cross-modal recognition method.

show abstract

Section: A Experimental Setup and Data Collectionmentioning

confidence: 99%

Deep Active Cross-Modal Visuo-Tactile Transfer Learning for Robotic Object Recognition

Murali

Wang

Lee

et al. 2022

IEEE Robot. Autom. Lett.

Self Cite

View full text Add to dashboard Cite

show abstract

Automatic Robot Hand-Eye Calibration Enabled by Learning-Based 3D Vision

Li,

Yang,

Wang

et al. 2024

J Intell Robot Syst

View full text Add to dashboard Cite

Hand-eye calibration, a fundamental task in vision-based robotic systems, is commonly equipped with collaborative robots, especially for robotic applications in small and medium-sized enterprises (SMEs). Most approaches to hand-eye calibration rely on external markers or human assistance. We proposed a novel methodology that addresses the hand-eye calibration problem using the robot base as a reference, eliminating the need for external calibration objects or human intervention. Using point clouds of the robot base, a transformation matrix from the coordinate frame of the camera to the robot base is established as “I=AXB.” To this end, we exploit learning-based 3D detection and registration algorithms to estimate the location and orientation of the robot base. The robustness and accuracy of the method are quantified by ground-truth-based evaluation, and the accuracy result is compared with other 3D vision-based calibration methods. To assess the feasibility of our methodology, we carried out experiments utilizing a low-cost structured light scanner across varying joint configurations and groups of experiments. The proposed hand-eye calibration method achieved a translation deviation of 0.930 mm and a rotation deviation of 0.265 degrees according to the experimental results. Additionally, the 3D reconstruction experiments demonstrated a rotation error of 0.994 degrees and a position error of 1.697 mm. Moreover, our method offers the potential to be completed in 1 second, which is the fastest compared to other 3D hand-eye calibration methods. We conduct indoor 3D reconstruction and robotic grasping experiments based on our hand-eye calibration method. Related code is released at https://github.com/leihui6/LRBO.

show abstract

Active Visuo-Tactile Point Cloud Registration for Accurate Pose Estimation of Objects in an Unknown Workspace

Murali

Gentner

Kaboli

2021

2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Self Cite

View full text Add to dashboard Cite

In Situ Translational Hand-Eye Calibration of Laser Profile Sensors using Arbitrary Objects

Cited by 5 publications

References 22 publications

Deep Active Cross-Modal Visuo-Tactile Transfer Learning for Robotic Object Recognition

Deep Active Cross-Modal Visuo-Tactile Transfer Learning for Robotic Object Recognition

Automatic Robot Hand-Eye Calibration Enabled by Learning-Based 3D Vision

Active Visuo-Tactile Point Cloud Registration for Accurate Pose Estimation of Objects in an Unknown Workspace

Contact Info

Product

Resources

About