Toward resilient manipulation of food products: Analysis of 6D-pose estimation at the Future Convenience Store Challenge 2022

Gustavo Alfonso Garcia Ricardez, Tatsuhiro Wakayama, Shohei Ikemura, Eri Fujiura, Pedro Miguel Uriguen Eljuri, Hiroki Ikeuchi, Masaki Yamamoto, Lotfi El Hafi, Tadahiro Taniguchi

Abstract: Service robots, the class of robots that are designed to assist humans in their daily lives, are needed in the retail industry to compensate for the labor shortage. To foster innovation, the Future Convenience Store Challenge was created, where robotic systems for the manipulation of food products are tasked to dispose of expired products and replenish the shelves. We, as team NAIST-RITS-Panasonic, have developed a mobile manipulator with which we have obtained 1st place in the past three editions of the challenge. In the last edition, we manipulated the five types of items without fiducial markers or customized packaging using a suction-based end effector. In this paper, we evaluate the accuracy of the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$6\mathrm{D}$</tex> -pose estimation as well as its effect on the grasping success rate by 1) comparing the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$6\mathrm{D}$</tex> -pose estimation results with the ground truth, and 2) evaluating the grasping success rate with the estimated pose during and after the competition. The results show that the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$6\mathrm{D}$</tex> -pose estimation error has a significant effect on the grasping success rate.