We present the first end-to-end solution to create high-quality freeviewpoint video encoded as a compact data stream. Our system records performances using a dense set of RGB and IR video cameras, generates dynamic textured surfaces, and compresses these to a streamable 3D video format. Four technical advances contribute to high fidelity and robustness: multimodal multi-view stereo fusing RGB, IR, and silhouette information; adaptive meshing guided by automatic detection of perceptually salient areas; mesh tracking to create temporally coherent subsequences; and encoding of tracked textured meshes as an MPEG video stream. Quantitative experiments demonstrate geometric accuracy, texture fidelity, and encoding efficiency. We release several datasets with calibrated inputs and processed results to foster future research.

We present MOPED, a framework for Multiple Object Pose Estimation and Detection that seamlessly integrates single-image and multi-image object recognition and pose estimation in one optimized, robust, and scalable framework. We address two main challenges in computer vision for robotics: robust performance in complex scenes, and low latency for real-time operation.We achieve robust performance with Iterative ClusteringEstimation (ICE), a novel algorithm that iteratively combines feature clustering with robust pose estimation. Feature clustering quickly partitions the scene and produces object hypotheses. The hypotheses are used to further refine the feature clusters, and the two steps iterate until convergence. ICE is easy to parallelize, and easily integrates single-and multi-camera object recognition and pose estimation. We also introduce a novel object hypothesis scoring function based on M-estimator theory, and a novel pose clustering algorithm that robustly handles recognition outliers.We achieve scalability and low latency with an improved feature matching algorithm for large databases, a GPU/CPU hybrid architecture that exploits parallelism at all levels, and an optimized resource scheduler. We provide extensive experimental results demonstrating state-of-the-art performance in terms of recognition, scalability, and latency in real-world robotic applications.

We describe the architecture, algorithms, and experiments with HERB, an autonomous mobile manipulator that performs useful manipulation tasks in the home. We present new algorithms for searching for objects, learning to navigate in cluttered dynamic indoor scenes, recognizing and registering objects accurately in high clutter using vision, manipulating doors and other constrained objects using caging grasps, grasp planning and execution in clutter, and manipulation on pose and torque constraint manifolds. We also present numerous severe real-world test results from the integration of these algorithms into a single mobile manipulator.

Abstract-Robust perception is a vital capability for robotic manipulation in unstructured scenes. In this context, full pose estimation of relevant objects in a scene is a critical step towards the introduction of robots into household environments. In this paper, we present an approach for building metric 3D models of objects using local descriptors from several images. Each model is optimized to fit a set of calibrated training images, thus obtaining the best possible alignment between the 3D model and the real object. Given a new test image, we match the local descriptors to our stored models online, using a novel combination of the RANSAC and Mean Shift algorithms to register multiple instances of each object. A robust initialization step allows for arbitrary rotation, translation and scaling of objects in the test images. The resulting system provides markerless 6-DOF pose estimation for complex objects in cluttered scenes. We provide experimental results demonstrating orientation and translation accuracy, as well a physical implementation of the pose output being used by an autonomous robot to perform grasping in highly cluttered scenes.

Abstract-We present an approach to path planning for manipulators that uses Workspace Goal Regions (WGRs) to specify goal end-effector poses. Instead of specifying a discrete set of goals in the manipulator's configuration space, we specify goals more intuitively as volumes in the manipulator's workspace. We show that WGRs provide a common framework for describing goal regions that are useful for grasping and manipulation. We also describe two randomized planning algorithms capable of planning with WGRs. The first is an extension of RRT-JT that interleaves exploration using a Rapidly-exploring Random Tree (RRT) with exploitation using Jacobian-based gradient descent toward WGR samples. The second is the IKBiRRT algorithm, which uses a forward-searching tree rooted at the start and a backward-searching tree that is seeded by WGR samples. We demonstrate both simulation and experimental results for a 7DOF WAM arm with a mobile base performing reaching and pick-and-place tasks. Our results show that planning with WGRs provides an intuitive and powerful method of specifying goals for a variety of tasks without sacrificing efficiency or desirable completeness properties.

Abstract-We present the hardware design, software architecture, and core algorithms of HERB 2.0, a bimanual mobile manipulator developed at the Personal Robotics Lab at Carnegie Mellon University. We have developed HERB 2.0 to perform useful tasks for and with people in human environments. We exploit two key paradigms in human environments: that they have structure that a robot can learn, adapt and exploit, and that they demand general-purpose capability in robotic systems. In this paper, we reveal some of the structure present in everyday environments that we have been able to harness for manipulation and interaction, comment on the particular challenges on working in human spaces, and describe some of the lessons we learned from extensively testing our integrated platform in kitchen and office environments.

We present a framework that retains ambiguity in feature matching to increase the performance of 3D object recognition systems. Whereas previous systems removed ambiguous correspondences during matching, we show that ambiguity should be resolved during hypothesis testing and not at the matching phase. To preserve ambiguity during matching, we vector quantize and match model features in a hierarchical manner. This matching technique allows our system to be more robust to the distribution of model descriptors in feature space. We also show that we can address recognition under arbitrary viewpoint by using our framework to facilitate matching of additional features extracted from affine transformed model images. The evaluation of our algorithms in 3D object recognition is demonstrated on a difficult dataset of 620 images.

Abstract-We present an approach for efficiently recognizing all objects in a scene and estimating their full pose from multiple views. Our approach builds upon a state of the art single-view algorithm which recognizes and registers learned metric 3D models using local descriptors. We extend to multiple views using a novel multi-step optimization that processes each view individually and feeds consistent hypotheses back to the algorithm for global refinement. We demonstrate that our method produces results comparable to the theoretical optimum, a full multi-view generalized camera approach, while avoiding its combinatorial time complexity. We provide experimental results demonstrating pose accuracy, speed, and robustness to model error using a three-camera rig, as well as a physical implementation of the pose output being used by an autonomous robot executing grasps in highly cluttered scenes.