To enable autonomous robotic manipulation in unstructured environments, we present SegICP-DSR, a real- time, dense, semantic scene reconstruction and pose estimation algorithm that achieves mm-level pose accuracy and standard deviation (7.9 mm, =7.6 mm and 1.7 deg, =0.7 deg) and suc- cessfully identified the object pose in 97 of test cases. This represents a 29 increase in accuracy, and a 14 increase in success rate compared to SegICP in cluttered, unstruc- tured environments. The performance increase of SegICP-DSR arises from (1) improved deep semantic segmentation under adversarial training, (2) precise automated calibration of the camera intrinsic and extrinsic parameters, (3) viewpoint specific ray-casting of the model geometry, and (4) dense semantic ElasticFusion point clouds for registration. We benchmark the performance of SegICP-DSR on thousands of pose-annotated video frames and demonstrate its accuracy and efficacy on two tight tolerance grasping and insertion tasks using a KUKA LBR iiwa robotic arm.
Accurate pose estimation in cluttered, unstructured environments is challenging, and many successful manipulation techniques (e.g. visual servoing) bypass the problem entirely. Several recent manipulation approaches have shown great success by avoiding an explicit representation of object pose and directly mapping raw sensor observations to motor behavior @cite_3 @cite_21 @cite_39 . However, these approaches do not provide the semantic expressiveness required for symbolic task planners @cite_16 , which are convenient for complex, long-horizon, multi-step manipulation tasks. Furthermore, object recognition and pose estimation provide an elegant, compact representation of high-dimensional sensor input. In this regard, we focus primarily on approaches that first solve the object-identification and pose-estimation problem and subsequently tackle manipulation.
@cite_16 @cite_21 @cite_3 @cite_39
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Pose estimation of deformable objects is a funda- mental and challenging problem in robotics. We present a novel solution to this problem by first reconstructing a 3D model of the object from a low-cost depth sensor such as Kinect, and then searching a database of simulated models in different poses to predict the pose. Given noisy depth images from 360- degree views of the target object acquired from the Kinect sensor, we reconstruct a smooth 3D model of the object using depth image segmentation and volumetric fusion. Then with an efficient feature extraction and matching scheme, we search the database, which contains a large number of deformable objects in different poses, to obtain the most similar model, whose pose is then adopted as the prediction. Extensive experiments demonstrate better accuracy and orders of magnitude speed- up compared to our previous work. An additional benefit of our method is that it produces a high-quality mesh model and camera pose, which is necessary for other tasks such as regrasping and object manipulation. I. INTRODUCTION In robotics and computer vision, recognition and ma- nipulation of deformable objects such as garments, are well-known challenging tasks. Recently, mature solutions to manipulating rigid objects have emerged and been applied in industry (3). However, in the fabric and food industry, which involve a large number of deformable objects, there is still a large gap between the high demand for automatic operations, and the lack of reliable solutions. Compared with rigid objects, deformable objects are much harder to recognize and manipulate, especially because of the large variance of appearance in materials and the way they deform. This variance subsequently makes it difficult to establish a robust recognition pipeline to predict the pose of the deformable objects based on traditional visual sensors, such as regular cameras. However, newly emerged low-cost depth sensors such as Microsoft Kinect can provide accurate depth measurements. With this depth information, a robotic system is able to resolve the ambiguity of visual appearance better, and thus provide higher performance on recognition tasks. Our interests are in detecting the pose of deformable objects such as garments as a part of a larger pipeline for manipulating these objects. Once the robot has identified the pose of the objects, it can then proceed to manipulate those objects, for tasks such as regarsping and garment folding. y indicates equal contribution We propose a novel approach for human pose estimation in real-world cluttered scenes, and focus on the challenging problem of predicting the pose of both arms for each person in the image. For this purpose, we build on the notion of poselets [4] and train highly discriminative classifiers to differentiate among arm configurations, which we call armlets. We propose a rich representation which, in addition to standard HOG features, integrates the information of strong contours, skin color and contextual cues in a principled manner. Unlike existing methods, we evaluate our approach on a large subset of images from the PASCAL VOC detection dataset, where critical visual phenomena, such as occlusion, truncation, multiple instances and clutter are the norm. Our approach outperforms Yang and Ramanan [26], the state-of-the-art technique, with an improvement from 29.0 to 37.5 PCP accuracy on the arm keypoint prediction task, on this new pose estimation dataset. Object detection and 6D pose estimation in the crowd (scenes with multiple object instances, severe foreground occlusions and background distractors), has become an important problem in many rapidly evolving technological areas such as robotics and augmented reality. Single shot-based 6D pose estimators with manually designed features are still unable to tackle the above challenges, motivating the research towards unsupervised feature learning and next-best-view estimation. In this work, we present a complete framework for both single shot-based 6D object pose estimation and next-best-view prediction based on Hough Forests, the state of the art object pose estimator that performs classification and regression jointly. Rather than using manually designed features we a) propose an unsupervised feature learnt from depth-invariant patches using a Sparse Autoencoder and b) offer an extensive evaluation of various state of the art features. Furthermore, taking advantage of the clustering performed in the leaf nodes of Hough Forests, we learn to estimate the reduction of uncertainty in other views, formulating the problem of selecting the next-best-view. To further improve pose estimation, we propose an improved joint registration and hypotheses verification module as a final refinement step to reject false detections. We provide two additional challenging datasets inspired from realistic scenarios to extensively evaluate the state of the art and our framework. One is related to domestic environments and the other depicts a bin-picking scenario mostly found in industrial settings. We show that our framework significantly outperforms state of the art both on public and on our datasets. We address the problem of estimating human pose in a single image using a part based approach. Pose accuracy is directly affected by the accuracy of the part detectors but more accurate detectors are likely to be also more computationally expensive. We propose to use multiple, heterogeneous part detectors with varying accuracy and computation requirements, ordered in a hierarchy, to achieve more accurate and efficient pose estimation. For inference, we propose an algorithm to localize articulated objects by exploiting an ordered hierarchy of detectors with increasing accuracy. The inference uses branch and bound method to search for each part and use kinematics from neighboring parts to guide the branching behavior and compute bounds on the best part estimate. We demonstrate our approach on a publicly available People dataset and outperform the state-of-art methods. Our inference is 3 times faster than one based on using a single, highly accurate detector.