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To tackle these challenges, we developed a deep-learning approach that combines object detection and semantic segmentation. The manipulation scenes are captured with RGB-D cameras, for which we developed a depth fusion method. Employing pretrained features makes learning from small annotated robotic datasets possible. We evaluate our approach on two challenging datasets: one captured for the Amazon Picking Challenge 2016, where our team NimbRo came in second in the Stowing and third in the Picking task; and one captured in disaster-response scenarios. The experiments show that object detection and semantic segmentation complement each other and can be combined to yield reliable object perception. <\/jats:p>","DOI":"10.1177\/0278364917713117","type":"journal-article","created":{"date-parts":[[2017,6,21]],"date-time":"2017-06-21T05:22:46Z","timestamp":1498022566000},"page":"437-451","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":118,"title":["RGB-D object detection and semantic segmentation for autonomous manipulation in clutter"],"prefix":"10.1177","volume":"37","author":[{"given":"Max","family":"Schwarz","sequence":"first","affiliation":[{"name":"University of Bonn, Germany"}]},{"given":"Anton","family":"Milan","sequence":"additional","affiliation":[{"name":"University of Adelaide, Australia"}]},{"given":"Arul Selvam","family":"Periyasamy","sequence":"additional","affiliation":[{"name":"University of Bonn, Germany"}]},{"given":"Sven","family":"Behnke","sequence":"additional","affiliation":[{"name":"University of Bonn, Germany"}]}],"member":"179","published-online":{"date-parts":[[2017,6,20]]},"reference":[{"key":"bibr1-0278364917713117","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2644615"},{"key":"bibr2-0278364917713117","doi-asserted-by":"publisher","DOI":"10.1007\/b11963"},{"key":"bibr3-0278364917713117","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2013.6738685"},{"key":"bibr4-0278364917713117","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2014.6906957"},{"key":"bibr5-0278364917713117","volume-title":"International Conference on Learning Representations (ICLR)","author":"Chen LC","year":"2015"},{"key":"bibr6-0278364917713117","unstructured":"Correll N, Bekris KE, Berenson D, et al. 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