LASSO: Location Assistant for Seeking and Searching Objects

Applying computer vision systems in robotic manufacturing to locate objects in various positions is an important approach in future manufacturing automation, e.g., product assembly tasks [1] and kitting tasks—grouping several parts to a container. However, the robot’s work environment is often large and cluttered with many objects, and this poses several challenges to computer vision systems. First, the accuracy of computer vision algorithms may be affected by cluttered backgrounds. Second, it is difficult to identify the target object when multiple objects are identical to the target. Third, using a single camera is not enough to cover all areas. To address the above challenges, we propose a programming system called LASSO (Location Assistant for Seeking and Searching Objects), which incorporates user hints to enhance multi-camera visual perception systems of robots in unstructured and cluttered environments. LASSO provides simple language for users to provide relative spatial relations as hints to narrow down the target object in its 3D space. LASSO then leverages this reduced search space to locate the target object in each camera view and compute the 3D location of the target object. A key feature of the LASSO system is that it quantifies the uncertainties of the 3D location, which provides users the quality of LASSO outputs. We demonstrate LASSO in both simulated and real world environments.

[1]  Nico Blodow,et al.  RoboSherlock: Unstructured information processing for robot perception , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[2]  Gregory D. Hager,et al.  CoSTAR: Instructing collaborative robots with behavior trees and vision , 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[3]  Morgan Quigley,et al.  ROS: an open-source Robot Operating System , 2009, ICRA 2009.

[4]  Andrew Howard,et al.  Design and use paradigms for Gazebo, an open-source multi-robot simulator , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[5]  Wei Liu,et al.  SSD: Single Shot MultiBox Detector , 2015, ECCV.

[6]  Vincent Lepetit,et al.  On Pre-Trained Image Features and Synthetic Images for Deep Learning , 2017, ECCV Workshops.

[7]  Dan Klein,et al.  Grounding spatial relations for human-robot interaction , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[8]  Koen E. A. van de Sande,et al.  Segmentation as selective search for object recognition , 2011, 2011 International Conference on Computer Vision.

[9]  Ewan Klein,et al.  Natural Language Processing with Python , 2009 .

[10]  Ankush Gupta,et al.  Synthetic Data for Text Localisation in Natural Images , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[12]  Gregory D. Hager,et al.  A framework for end-user instruction of a robot assistant for manufacturing , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[13]  Ali Farhadi,et al.  YOLO9000: Better, Faster, Stronger , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Sven Behnke,et al.  A skill-based system for object perception and manipulation for automating kitting tasks , 2015, 2015 IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA).

[15]  Risto Miikkulainen,et al.  Tradeoffs in Neuroevolutionary Learning-Based Real-Time Robotic Task Design in the Imprecise Computation Framework , 2018, ACM Trans. Cyber Phys. Syst..

[16]  Aloysius K. Mok,et al.  A Skill-Based Programming System for Robotic Furniture Assembly , 2018, 2018 IEEE 16th International Conference on Industrial Informatics (INDIN).

[17]  Francisco José Madrid-Cuevas,et al.  Automatic generation and detection of highly reliable fiducial markers under occlusion , 2014, Pattern Recognit..