{"title":"在ICRA感知挑战11解决方案的背景下,雅各布斯机器人技术对物体识别和定位的方法","authors":"N. Vaskevicius, K. Pathak, A. Ichim, A. Birk","doi":"10.1109/ICRA.2012.6225335","DOIUrl":null,"url":null,"abstract":"In this paper, we give an overview of the Jacobs Robotics entry to the ICRA'11 Solutions in Perception Challenge. We present our multi-pronged strategy for object recognition and localization based on the integrated geometric and visual information available from the Kinect sensor. Firstly, the range image is over-segmented using an edge-detection algorithm and regions of interest are extracted based on a simple shape-analysis per segment. Then, these selected regions of the scene are matched with known objects using visual features and their distribution in 3D space. Finally, generated hypotheses about the positions of the objects are tested by back-projecting learned 3D models to the scene using estimated transformations and sensor model. Our method won the second place among eight competing algorithms, only marginally losing to the winner.","PeriodicalId":246173,"journal":{"name":"2012 IEEE International Conference on Robotics and Automation","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"26","resultStr":"{\"title\":\"The jacobs robotics approach to object recognition and localization in the context of the ICRA'11 Solutions in Perception Challenge\",\"authors\":\"N. Vaskevicius, K. Pathak, A. Ichim, A. Birk\",\"doi\":\"10.1109/ICRA.2012.6225335\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we give an overview of the Jacobs Robotics entry to the ICRA'11 Solutions in Perception Challenge. We present our multi-pronged strategy for object recognition and localization based on the integrated geometric and visual information available from the Kinect sensor. Firstly, the range image is over-segmented using an edge-detection algorithm and regions of interest are extracted based on a simple shape-analysis per segment. Then, these selected regions of the scene are matched with known objects using visual features and their distribution in 3D space. Finally, generated hypotheses about the positions of the objects are tested by back-projecting learned 3D models to the scene using estimated transformations and sensor model. Our method won the second place among eight competing algorithms, only marginally losing to the winner.\",\"PeriodicalId\":246173,\"journal\":{\"name\":\"2012 IEEE International Conference on Robotics and Automation\",\"volume\":\"25 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-05-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"26\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE International Conference on Robotics and Automation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICRA.2012.6225335\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE International Conference on Robotics and Automation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICRA.2012.6225335","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The jacobs robotics approach to object recognition and localization in the context of the ICRA'11 Solutions in Perception Challenge
In this paper, we give an overview of the Jacobs Robotics entry to the ICRA'11 Solutions in Perception Challenge. We present our multi-pronged strategy for object recognition and localization based on the integrated geometric and visual information available from the Kinect sensor. Firstly, the range image is over-segmented using an edge-detection algorithm and regions of interest are extracted based on a simple shape-analysis per segment. Then, these selected regions of the scene are matched with known objects using visual features and their distribution in 3D space. Finally, generated hypotheses about the positions of the objects are tested by back-projecting learned 3D models to the scene using estimated transformations and sensor model. Our method won the second place among eight competing algorithms, only marginally losing to the winner.