Abstract-Modern approaches to simultaneous localization and mapping (SLAM) formulate the inference problem as a highdimensional but sparse nonconvex M-estimation, and then apply general first-or second-order smooth optimization methods to recover a local minimizer of the objective function. The performance of any such approach depends crucially upon initializing the optimization algorithm near a good solution for the inference problem, a condition that is often difficult or impossible to guarantee in practice. To address this limitation, in this paper we present a formulation of the SLAM M-estimation with the property that, by expanding the feasible set of the estimation program, we obtain a convex relaxation whose solution approximates the globally optimal solution of the SLAM inference problem and can be recovered using a smooth optimization method initialized at any feasible point. Our formulation thus provides a means to obtain a high-quality solution to the SLAM problem without requiring high-quality initialization.

Abstract-We describe our efforts to create infrastructure to enable web interfaces for robotics. Such interfaces will enable researchers and users to remotely access robots through the internet as well as expand the types of robotic applications available to users with web-enabled devices. This paper centers on rosjs, a lightweight Javascript binding for ROS, Willow Garage's robot middleware framework. rosjs exposes many of the capabilities of ROS, allowing application developers to write controllers that are executed through a web browser. We discuss how rosjs extends ROS and briefly overview some of the features it provides. rosjs has been instrumental in the creation of remote laboratories featuring the iRobot Create and the PR2. These facilities will be available to the community as experimental resources. We describe the overall goals of this project as well as provide a brief description of how rosjs was used to help create web interfaces for these facilities.

Abstract-Reliability and availability are major concerns for autonomous systems. A personal robot has to solve complex tasks, such as loading a dishwasher or folding laundry, which are very difficult to automate robustly. In order for a robot to perform better in those applications, it needs to be capable of accepting help from a human operator.Shared autonomy is a system model based on human-robot dialogue. This work aims at bridging the gap between full human control and full autonomy for tasks in the domain of personal robotics. One of the hardest problems for personal robotic systems is perception: perceiving and inferring about objects in the robot's environment. We present a system capable of solving the perceptual inference in combination with a human, such that a human operator functions as a resource for the robot and helps to compensate for limitations of autonomy.In this paper, we show how a human-robot team can work together effectively to solve complex perception tasks. We present a system that asks a human operator to identify objects it doesn't recognize or find. In various experiments with the PR2 robot we show that this shared autonomy system performs more robustly than the robot system alone and that it is capable of tasks which are difficult to accomplish by an autonomous agent.

Abstract3D reconstruction and visualization of environments is increasingly important and there is a wide range of application areas where 3D models are required. Reconstructing 3D models has therefore been a major research focus in academia and industry. For example, large scale efforts for the reconstruction of city models at a global scale are currently underway. A major limitation in those efforts is that creating realistic 3D models of environments is a tedious and time consuming task. In particular, two major issues persist which prevent a broader adoption of 3D modeling techniques: a lack of affordable 3D scanning devices that enable an easy acquisition of 3D data and algorithms capable of automatically processing this data into 3D models. We believe that autonomous technologies, which are capable of generating textured 3D models of real environments, will make the modeling process affordable and enable a wide variety of new applications. This thesis addresses the problem of automatic 3D reconstruction and we present a system for unsupervised reconstruction of textured 3D models in the context of modeling indoor environments. The contributions are solutions to all aspects of the modeling process and an integrated system for the automatic creation of large scale 3D models. We first present a robotic data acquisition system which allows us to automatically scan large environments in a short amount of time. We also propose a calibration procedure for this system that determines the internal and external calibration which is necessary to transform data from one sensor into the coordinate system of another sensor. Next, we present solutions for the multi-view data registration problem, which is essentially the problem of aligning the data of multiple 3D scans into a common coordinate system. We propose a novel nonrigid registration method based on a probabilistic SLAM framework. This method incorporates spatial correlation models as map priors to guide the optimization. Scans are aligned by optimizing robot pose estimates as well as scan points. We show that this non-rigid registration significantly improves the alignment. Next, we address the problem of reconstructing a consistent 3D surface representation from the registered point clouds. We propose a volumetric surface reconstruction method based on a Poisson framework. In a second step, we improve the accuracy of this reconstruction by optimizing the mesh vertices to achieve a better approximation of the true surface. We demonstrate that this method is very suitable for the reconstruction of indoor environments. Finally, we present a solution to the reconstruction of texture maps from multiple scans. Our texture reconstruction approach partitions the surface into segments, unfolds each segment onto a plane, and reconstructs a texture map by blending multiple views into a single composite. This technique results in a very realistic reconstruction of the surface appearance and greatly enhances the visual impression by adding more realism.

Abstract-Online video presents a great opportunity for upand-coming singers and artists to be visible to a worldwide audience. However, the sheer quantity of video makes it difficult to discover promising musicians. We present a novel algorithm to automatically identify talented musicians using machine learning and acoustic analysis on a large set of "home singing" videos. We describe how candidate musician videos are identified and ranked by singing quality. To this end, we present new audio features specifically designed to directly capture singing quality. We evaluate these vis-a-vis a large set of generic audio features and demonstrate that the proposed features have good predictive performance. We also show that this algorithm performs well when videos are normalized for production quality.