{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-dataset-for-improved-rgbd-based-object","title":"A Dataset for Improved RGBD-based Object Detection and Pose Estimation for Warehouse Pick-and-Place","arxiv_id":"1509.01277","date":"2015-09-03","proceeding":null,"authors":["Colin Rennie","Rahul Shome","Kostas E. Bekris","Alberto F. de Souza"],"abstract":"An important logistics application of robotics involves manipulators that\npick-and-place objects placed in warehouse shelves. A critical aspect of this\ntask corre- sponds to detecting the pose of a known object in the shelf using\nvisual data. Solving this problem can be assisted by the use of an RGB-D\nsensor, which also provides depth information beyond visual data. Nevertheless,\nit remains a challenging problem since multiple issues need to be addressed,\nsuch as low illumination inside shelves, clutter, texture-less and reflective\nobjects as well as the limitations of depth sensors. This paper provides a new\nrich data set for advancing the state-of-the-art in RGBD- based 3D object pose\nestimation, which is focused on the challenges that arise when solving\nwarehouse pick- and-place tasks. The publicly available data set includes\nthousands of images and corresponding ground truth data for the objects used\nduring the first Amazon Picking Challenge at different poses and clutter\nconditions. Each image is accompanied with ground truth information to assist\nin the evaluation of algorithms for object detection. To show the utility of\nthe data set, a recent algorithm for RGBD-based pose estimation is evaluated in\nthis paper. Based on the measured performance of the algorithm on the data set,\nvarious modifications and improvements are applied to increase the accuracy of\ndetection. These steps can be easily applied to a variety of different\nmethodologies for object pose detection and improve performance in the domain\nof warehouse pick-and-place.","url_abs":"http://arxiv.org/abs/1509.01277v2","url_pdf":"http://arxiv.org/pdf/1509.01277v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[{"slug":"ru-apc","name":"RU-APC","full_name":"Rutgers APC"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1509.01277","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}