Point Pair Feature based Object Detection for Random Bin Picking

Point pair features are a popular representation for free form 3D object detection and pose estimation. In this paper, their performance in an industrial random bin picking context is investigated. A new method to generate representative synthetic datasets is proposed. This allows to investigate the influence of a high degree of clutter and the presence of self similar features, which are typical to our application. We provide an overview of solutions proposed in literature and discuss their strengths and weaknesses. A simple heuristic method to drastically reduce the computational complexity is introduced, which results in improved robustness, speed and accuracy compared to the naive approach

The Dialectic of the Theology of Browning\u27s Bishop Blougram

Detecting and tracking people in images is an attractive method to monitor their movements. It is based on passive, non-contact sensors and hence does not disturb or distract the subjects. The analysis of the extracted position and pose data can be used in applications such as security and safety monitoring, home automation, patient monitoring or behavior analysis. However, detecting people in images is a challenging problem. Differences in pose, clothing and lighting (along with other factors) cause a lot of variation in their appearance. Some solutions have been proposed but they typically have mediocre accuracy, suffer from severe limitations, require large amounts of annotated training data and are computationally expensive. To overcome these issues, we propose a system based on fused range and thermal infrared images. These measurements show considerably less variation and provide more meaningful information. We provide a brief introduction to the sensor technology used and propose a calibration method. Several data fusion algorithms are compared and their performance is assessed on a simulated data set. The results of initial experiments are shown and the measurement errors and the challenges they present are discussed. The resulting fused data are used to efficiently detect people in a fixed camera setup. The system can be extended to include person tracking.status: publishe

Embedded Line Scan Image Sensors: The Low Cost Alternative for High Speed Imaging

In this paper we propose a low-cost high-speed imaging line scan system. We replace an expensive industrial line scan camera and illumination with a custom-built set-up of cheap off-the-shelf components, yielding a measurement system with comparative quality while costing about 20 times less. We use a low-cost linear (1D) image sensor, cheap optics including a LED-based or LASER-based lighting and an embedded platform to process the images. A step-by-step method to design such a custom high speed imaging system and select proper components is proposed. Simulations allowing to predict the final image quality to be obtained by the set-up has been developed. Finally, we applied our method in a lab, closely representing the real-life cases. Our results shows that our simulations are very accurate and that our low-cost line scan set-up acquired image quality compared to the high-end commercial vision system, for a fraction of the price.Comment: 2015 International Conference on Image Processing Theory, Tools and Applications (IPTA

3D Object Discovery and Modeling Using Single RGB-D Images Containing Multiple Object Instances

Unsupervised object modeling is important in robotics, especially for handling a large set of objects. We present a method for unsupervised 3D object discovery, reconstruction, and localization that exploits multiple instances of an identical object contained in a single RGB-D image. The proposed method does not rely on segmentation, scene knowledge, or user input, and thus is easily scalable. Our method aims to find recurrent patterns in a single RGB-D image by utilizing appearance and geometry of the salient regions. We extract keypoints and match them in pairs based on their descriptors. We then generate triplets of the keypoints matching with each other using several geometric criteria to minimize false matches. The relative poses of the matched triplets are computed and clustered to discover sets of triplet pairs with similar relative poses. Triplets belonging to the same set are likely to belong to the same object and are used to construct an initial object model. Detection of remaining instances with the initial object model using RANSAC allows to further expand and refine the model. The automatically generated object models are both compact and descriptive. We show quantitative and qualitative results on RGB-D images with various objects including some from the Amazon Picking Challenge. We also demonstrate the use of our method in an object picking scenario with a robotic arm

Detecting and Grouping Identical Objects for Region Proposal and Classification

Often multiple instances of an object occur in the same scene, for example in a warehouse. Unsupervised multi-instance object discovery algorithms are able to detect and identify such objects. We use such an algorithm to provide object proposals to a convolutional neural network (CNN) based classifier. This results in fewer regions to evaluate, compared to traditional region proposal algorithms. Additionally, it enables using the joint probability of multiple instances of an object, resulting in improved classification accuracy. The proposed technique can also split a single class into multiple sub-classes corresponding to the different object types, enabling hierarchical classification.Comment: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Workshop Deep Learning for Robotic Vision, 21 July, 2017, Honolulu, Hawai

Robotic picking system demonstration

During the Factory-in-a-Day event at RoboBusiness Europe on June 1st, Team Delft and Team Applied Robots will demonstrate their robotic solutions for this year’s finals of the Amazon Picking Challenge in Leipzig end of June. In an exhibition match between the two teams, the robots will compete against each other to be the best at picking products off a dual-sided Amazon shelf, showing off their intelligent perception and motion skills. With less than a month before the finals, at RoboCup (Leipzig, June 29 – July 3), Team Delft and Team Applied Robots welcome the opportunity to share the progress of their robots at a public exhibition. The challenge involves picking and stowing products off a shelf in an Amazon warehouse. This unstructured scenario, with heterogeneous objects and high uncertainty, remains a challenge for robot automation. It requires advanced skills, such as object recognition and localization, motion and grasp planning, intelligent task coordination and reliable error detection and recovery.status: publishe

Point Pair Feature based Object Detection for Random Bin Picking

Point pair features are a popular representation for free form 3D object detection and pose estimation. In this paper, their performance in an industrial random bin picking context is investigated. A new method to generate representative synthetic datasets is proposed. This allows to investigate the influence of a high degree of clutter and the presence of self similar features, which are typical to our application. We provide an overview of solutions proposed in literature and discuss their strengths and weaknesses. A simple heuristic method to drastically reduce the computational complexity is introduced, which results in improved robustness, speed and accuracy compared to the naive approach.status: publishe

Exploring the potential of combining time of flight and thermal infrared cameras for person detection

Combining new, low-cost thermal infrared and time-of-flight range sensors provides new opportunities. In this position paper we explore the possibilities of combining these sensors and using their fused data for person detection. The proposed calibration approach for this sensor combination differs from the traditional stereo camera calibration in two fundamental ways. A first distinction is that the spectral sensitivity of the two sensors differs significantly. In fact, there is no sensitivity range overlap at all. A second distinction is that their resolution is typically very low, which requires special attention. We assume a situation in which the sensors’ relative position is known, but their orientation is unknown. In addition, some of the typical measurement errors are discussed, and methods to compensate for them are proposed. We discuss how the fused data could allow increased accuracy and robustness without the need for complex algorithms requiring large amounts of computational power and training data.status: publishe

Vision Guided Random Picking for Industrial Robots

The goal of the RaPiDo project is to enable industrial robots to handle objects that are positioned randomly. There are two important issues that need to be resolved. The first is to detect the object to handle, and measure it’s position. The second challenge is to calculate a trajectory the robot can follow towards this object, to pick it up.status: publishe