CAD-based recognition of 3D objects in monocular images

Ulrich, Markus; Wiedemann, Christian; Steger, Carsten

doi:10.1109/robot.2009.5152511

Cited by 96 publications

(69 citation statements)

References 20 publications

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“…Model-based approaches to determine camera poses from imagery are also available (Reitmayr and Drummond, 2006;Unger et al, 2016). Determining the camera pose in real time, in indoor environments, is practicable by CAD model matching (Ulrich et al, 2009;Zang and Hashimoto, 2011;Urban et al, 2013;Mueller and Voegtle, 2016). Convolutional neural networks are used to determine matches between aerial images and UAV images (Altwaijry et al, 2016) or terrestrial images and UAV images (Lin et al, 2015).…”

Section: Related Workmentioning

confidence: 99%

Squeezeposenet: Image Based Pose Regression With Small Convolutional Neural Networks for Real Time Uas Navigation

Müller

Urban

Jutzi

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The number of unmanned aerial vehicles (UAVs) is increasing since low-cost airborne systems are available for a wide range of users. The outdoor navigation of such vehicles is mostly based on global navigation satellite system (GNSS) methods to gain the vehicles trajectory. The drawback of satellite-based navigation are failures caused by occlusions and multi-path interferences. Beside this, local image-based solutions like Simultaneous Localization and Mapping (SLAM) and Visual Odometry (VO) can e.g. be used to support the GNSS solution by closing trajectory gaps but are computationally expensive. However, if the trajectory estimation is interrupted or not available a re-localization is mandatory. In this paper we will provide a novel method for a GNSS-free and fast image-based pose regression in a known area by utilizing a small convolutional neural network (CNN). With on-board processing in mind, we employ a lightweight CNN called SqueezeNet and use transfer learning to adapt the network to pose regression. Our experiments show promising results for GNSS-free and fast localization.

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Section: Related Workmentioning

confidence: 99%

Squeezeposenet: Image Based Pose Regression With Small Convolutional Neural Networks for Real Time Uas Navigation

Müller

Urban

Jutzi

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…It is based on existing CAD-based approaches to pose estimation (Ulrich et al [27], Liu et al [15]) but our algorithm doesn't need a CAD model and utilizes the specifics of transparent objects to meet practical requirements in performance and accuracy.…”

Section: Proposed Approachmentioning

confidence: 99%

Recognition and Pose Estimation of Rigid Transparent Objects with a Kinect Sensor

Lysenkov¹,

Eruhimov²

2012

Robotics: Science and Systems VIII

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Abstract-Recognizing and determining the 6DOF pose of transparent objects is necessary in order for robots to manipulate such objects. However, it is a challenging problem for computer vision. We propose new algorithms for segmentation, pose estimation and recognition of transparent objects from a single RGB-D image from a Kinect sensor. Kinect's weakness in the perception of transparent objects is exploited in their segmentation. Following segmentation, edge fitting is used for recognition and pose estimation. A 3D model of the object is created automatically during training and it is required for pose estimation and recognition.The algorithm is evaluated in different conditions of a domestic environment within the framework of a robotic grasping pipeline where it demonstrates high grasping success rates compared to the state-of-the-art results. The method doesn't deal with occlusions and overlapping transparent objects currently but it is robust against non-transparent clutter.

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“…Technology related with the reference software can be found in Ulrich et al (2009), which relies on edge-based fast matching scheme. Here we show some results in Figure 8.…”

Section: Benchmarkingmentioning

confidence: 99%

“…To date, however, there has been only limited success in this longstanding problem (e.g., picking piled objects), and, to the best of our knowledge, existing algorithms (e.g., Drost, et al, 2010;Ulrich et al, 2009;Hinterstoisser et al, 2007) fall short of practical use in automatic assembly of electronic products composed of various parts with differing optical and surface properties as well as with differing shapes and sizes. We found that even the stateof-the-art, commercially available machine vision software cannot be practically used for picking such piles of parts with unknown pose and occlusion.…”

Section: Introductionmentioning

confidence: 99%

Detection and Pose Estimation of Piled Objects Using Ensemble of Tree Classifiers

Matsugu¹,

Mori²,

Mitarai³

et al. 2012

Machine Vision - Applications and Systems

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CAD-based recognition of 3D objects in monocular images

Cited by 96 publications

References 20 publications

Squeezeposenet: Image Based Pose Regression With Small Convolutional Neural Networks for Real Time Uas Navigation

Squeezeposenet: Image Based Pose Regression With Small Convolutional Neural Networks for Real Time Uas Navigation

Recognition and Pose Estimation of Rigid Transparent Objects with a Kinect Sensor

Detection and Pose Estimation of Piled Objects Using Ensemble of Tree Classifiers

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