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Dellaert, Frank

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Author: Ok, Kyel × End date: 2015 × Start date: 2012 ×

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Now showing 1 - 6 of 6
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    Monocular Image Space Tracking on a Computationally Limited MAV
    (Georgia Institute of Technology, 2015-05) Ok, Kyel ; Gamage, Dinesh ; Drummond, Tom ; Dellaert, Frank ; Roy, Nicholas
    We propose a method of monocular camera-inertial based navigation for computationally limited micro air vehicles (MAVs). Our approach is derived from the recent development of parallel tracking and mapping algorithms, but unlike previous results, we show how the tracking and mapping processes operate using different representations.The separation of representations allows us not only to move the computational load of full map inference to a ground station, but to further reduce the computational cost of on-board tracking for pose estimation. Our primary contribution is to show how the cost of tracking the vehicle pose on-board can be substantially reduced by estimating the camera motion directly in the image frame, rather than in the world co-ordinate frame. We demonstrate our method on an Ascending Technologies Pelican quad-rotor, and show that we can track the vehicle pose with reduced on-board computation but without compromised navigation accuracy.
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    Monocular Parallel Tracking and Mapping with Odometry Fusion for MAV Navigation in Feature-Lacking Environments
    (Georgia Institute of Technology, 2013-11) Ta, Duy-Nguyen ; Ok, Kyel ; Dellaert, Frank
    Despite recent progress, autonomous navigation on Micro Aerial Vehicles with a single frontal camera is still a challenging problem, especially in feature-lacking environ- ments. On a mobile robot with a frontal camera, monoSLAM can fail when there are not enough visual features in the scene, or when the robot, with rotationally dominant motions, yaws away from a known map toward unknown regions. To overcome such limitations and increase responsiveness, we present a novel parallel tracking and mapping framework that is suitable for robot navigation by fusing visual data with odometry measurements in a principled manner. Our framework can cope with a lack of visual features in the scene, and maintain robustness during pure camera rotations. We demonstrate our results on a dataset captured from the frontal camera of a quad- rotor flying in a typical feature-lacking indoor environment.
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    Path Planning with Uncertainty: Voronoi Uncertainty Fields
    (Georgia Institute of Technology, 2013-05) Ok, Kyel ; Ansari, Sameer ; Gallagher, Billy ; Sica, William ; Dellaert, Frank ; Stilman, Mike
    In this paper, a two-level path planning algorithm that deals with map uncertainty is proposed. The higher level planner uses modified generalized Voronoi diagrams to guarantee finding a connected path from the start to the goal if a collision-free path exists. The lower level planner considers uncertainty of the observed obstacles in the environment and assigns repulsive forces based on their distance to the robot and their positional uncertainty. The attractive forces from the Voronoi nodes and the repulsive forces from the uncertainty- biased potential fields form a hybrid planner we call Voronoi Uncertainty Fields (VUF). The proposed planner has two strong properties: (1) bias against uncertain obstacles, and (2) completeness. We analytically prove the properties and run simulations to validate our method in a forest-like environment.
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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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    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.