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Dellaert, Frank

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End date: 2012 × Start date: 2012 × Item Type: Publication × search.filters.applied.f.isOrgUnitOfPublicationTitle: College of Computing ×

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Now showing 1 - 7 of 7
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    Accurate On-Line 3D Occupancy Grids Using Manhattan World Constraints
    (Georgia Institute of Technology, 2012-10) Peasley, Brian ; Birchfield, Stan ; Cunningham, Alexander ; Dellaert, Frank
    In this paper we present an algorithm for constructing nearly drift-free 3D occupancy grids of large indoor environments in an online manner. Our approach combines data from an odometry sensor with output from a visual registration algorithm, and it enforces a Manhattan world constraint by utilizing factor graphs to produce an accurate online estimate of the trajectory of a mobile robotic platform. We also examine the advantages and limitations of the octree data structure representation of a 3D environment. Through several experiments in environments with varying sizes and construction we show that our method reduces rotational and translational drift significantly without performing any loop closing techniques.
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    Vistas and Wall-Floor Intersection Features: Enabling Autonomous Flight in Man-made Environments
    (Georgia Institute of Technology, 2012-10) Ok, Kyel ; Ta, Duy-Nguyen ; Dellaert, Frank
    We propose a solution toward the problem of autonomous flight and exploration in man-made indoor environments with a micro aerial vehicle (MAV), using a frontal camera, a downward-facing sonar, and an IMU. We present a general method to detect and steer an MAV toward distant features that we call vistas while building a map of the environment to detect unexplored regions. Our method enables autonomous exploration capabilities while working reliably in textureless indoor environments that are challenging for traditional monocular SLAM approaches. We overcome the difficulties faced by traditional approaches with Wall-Floor Intersection Features , a novel type of low-dimensional landmarks that are specifically designed for man-made environments to capture the geometric structure of the scene. We demonstrate our results on a small, commercially available quadrotor platform.
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    Robotics and Vision
    (Georgia Institute of Technology, 2012-08-28) Dellaert, Frank
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    Attitude Heading Reference System with Rotation-Aiding Visual Landmarks
    (Georgia Institute of Technology, 2012-07) Beall, Chris ; Ta, Duy-Nguyen ; Ok, Kyel ; Dellaert, Frank
    In this paper we present a novel vision-aided attitude heading reference system for micro aerial vehicles (MAVs) and other mobile platforms, which does not rely on known landmark locations or full 3D map estimation as is common in the literature. Inertial sensors which are commonly found on MAVs suffer from additive biases and noise, and yaw error will grow without bounds. The bearing-only measurements, which we call vistas, aid the vehicle’s heading estimate and allow for long-term operation while correcting for sensor drift. Our method is experimentally validated on a commercially available low-cost quadrotor MAV.
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    Saliency Detection and Model-based Tracking: a Two Part Vision System for Small Robot Navigation in Forested Environments
    (Georgia Institute of Technology, 2012-05-01) Roberts, Richard ; Ta, Duy-Nguyen ; Straub, Julian ; Ok, Kyel ; Dellaert, Frank
    Towards the goal of fast, vision-based autonomous flight, localization, and map building to support local planning and control in unstructured outdoor environments, we present a method for incrementally building a map of salient tree trunks while simultaneously estimating the trajectory of a quadrotor flying through a forest. We make significant progress in a class of visual perception methods that produce low-dimensional, geometric information that is ideal for planning and navigation on aerial robots, while directing computational resources using motion saliency, which selects objects that are important to navigation and planning. By low-dimensional geometric information, we mean coarse geometric primitives, which for the purposes of motion planning and navigation are suitable proxies for real-world objects. Additionally, we develop a method for summarizing past image measurements that avoids expensive computations on a history of images while maintaining the key non-linearities that make full map and trajectory smoothing possible. We demonstrate results with data from a small, commercially-available quad-rotor flying in a challenging, forested environment.
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    Planar Segmentation of RGBD Images Using Fast Linear Fitting and Markov Chain Monte Carlo
    (Georgia Institute of Technology, 2012-05) Erdogan, Can ; Paluri, Manohar ; Dellaert, Frank
    With the advent of affordable RGBD sensors such as the Kinect, the collection of depth and appearance information from a scene has become effortless. However, neither the correct noise model for these sensors, nor a principled methodology for extracting planar segmentations has been developed yet. In this work, we advance the state of art with the following contributions: we correctly model the Kinect sensor data by observing that the data has inherent noise only over the measured disparity values, we formulate plane fitting as a linear least-squares problem that allow us to quickly merge different segments, and we apply an advanced Markov Chain Monte Carlo (MCMC) method, generalized Swendsen-Wang sampling, to efficiently search the space of planar segmentations.We evaluate our plane fitting and surface reconstruction algorithms with simulated and real-world data.
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    Envisioning: Mental Rotation-based Semi-reactive Robot Control
    (Georgia Institute of Technology, 2012) Arkin, Ronald C. ; Dellaert, Frank ; Devassy, Joan
    This paper describes ongoing research into the role of optic-flow derived spatial representations and their relation to cognitive computational models of mental rotation in primates, with the goal of producing effective and unique autonomous robot navigational capabilities. A theoretical framework is outlined based on a vectorial interlingua spanning perception, cognition and motor control. Progress to date on its implementation within an autonomous robot control architecture is presented.