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16
Autonomous mav flight in indoor environments using single image perspective cues
- IN: INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA
, 2011
"... We consider the problem of autonomously flying Miniature Aerial Vehicles (MAVs) in indoor environments such as home and office buildings. The primary long range sensor in these MAVs is a miniature camera. While previous approaches first try to build a 3D model in order to do planning and control, o ..."
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We consider the problem of autonomously flying Miniature Aerial Vehicles (MAVs) in indoor environments such as home and office buildings. The primary long range sensor in these MAVs is a miniature camera. While previous approaches first try to build a 3D model in order to do planning and control, our method neither attempts to build nor requires a 3D model. Instead, our method first classifies the type of indoor environment the MAV is in, and then uses vision algorithms based on perspective cues to estimate the desired direction to fly. We test our method on two MAV platforms: a co-axial miniature helicopter and a toy quadrotor. Our experiments show that our vision algorithms are quite reliable, and they enable our MAVs to fly in a variety of corridors and staircases.
Autonomous obstacle avoidance and maneuvering on a vision-guided mav using on-board processing
- In Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA
, 2011
"... Abstract — We present a novel stereo-based obstacle avoid-ance system on a vision-guided micro air vehicle (MAV) that is capable of fully autonomous maneuvers in unknown and dynamic environments. All algorithms run exclusively on the vehicle’s on-board computer, and at high frequencies that allow th ..."
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Cited by 15 (7 self)
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Abstract — We present a novel stereo-based obstacle avoid-ance system on a vision-guided micro air vehicle (MAV) that is capable of fully autonomous maneuvers in unknown and dynamic environments. All algorithms run exclusively on the vehicle’s on-board computer, and at high frequencies that allow the MAV to react quickly to obstacles appearing in its flight trajectory. Our MAV platform is a quadrotor aircraft equipped with an inertial measurement unit and two stereo rigs. An obstacle mapping algorithm processes stereo images, producing a 3D map representation of the environment; at the same time, a dynamic anytime path planner plans a collision-free path to a goal point. I.
Indoor Navigation with a Swarm of Flying Robots
"... Abstract — Swarms of flying robots are promising in many applications due to rapid terrain coverage. However, there are numerous challenges in realising autonomous operation in unknown indoor environments. A new autonomous flight methodology is presented using relative positioning sensors in referen ..."
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Cited by 3 (0 self)
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Abstract — Swarms of flying robots are promising in many applications due to rapid terrain coverage. However, there are numerous challenges in realising autonomous operation in unknown indoor environments. A new autonomous flight methodology is presented using relative positioning sensors in reference to nearby static robots. The entirely decentralised approach relies solely on local sensing without requiring absolute positioning, environment maps, powerful computation or longrange communication. The swarm deploys as a robotic network facilitating navigation and goal directed flight. Initial validation tests with quadrotors demonstrated autonomous flight within a confined indoor environment, indicating that they could traverse a large network of static robots across expansive environments. I.
Motion Estimation of a Miniature Helicopter using a Single Onboard Camera,” American Control Conference
, 2010
"... Abstract — This paper describes a technique for the estima-tion of the translational and rotational velocities of a miniature helicopter using the video signals from a single onboard camera. For every two consecutive frames from the camera, point correspondences are identified and Epipolar Geometry ..."
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Abstract — This paper describes a technique for the estima-tion of the translational and rotational velocities of a miniature helicopter using the video signals from a single onboard camera. For every two consecutive frames from the camera, point correspondences are identified and Epipolar Geometry based algorithms are used to find the likely estimates of the absolute rotations and relative displacements. Images from onboard camera are often corrupted with various types of noises; SIFT descriptors were found to be the best feature descriptors to be used for point correspondences. To speed up the processing, we introduce a new representation of these descriptors based on compressive sensing formalisms. To estimate the absolute displacement of the helicopter between frames, we use the measurements from a simulated IR sensor to find the true change in altitude of the body frame, scaling other translational dimensions accordingly, and later estimating the velocities. Experiments conducted using data from a real helicopter in an indoor environment demonstrate promising results. I.
Saliency detection and model-based tracking: a two part vision system for small robot navigation in forested environment
- in Proceedings of SPIE
, 2012
"... 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 quadrot ..."
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Cited by 2 (0 self)
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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. 1.
Low-Power Parallel Algorithms for Single Image based Obstacle Avoidance in Aerial Robots
"... Abstract — For an aerial robot, perceiving and avoiding obstacles are necessary skills to function autonomously in a cluttered unknown environment. In this work, we use a single image captured from the onboard camera as input, produce obstacle classifications, and use them to select an evasive maneu ..."
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Abstract — For an aerial robot, perceiving and avoiding obstacles are necessary skills to function autonomously in a cluttered unknown environment. In this work, we use a single image captured from the onboard camera as input, produce obstacle classifications, and use them to select an evasive maneuver. We present a Markov Random Field based approach that models the obstacles as a function of visual features and non-local dependencies in neighboring regions of the image. We perform efficient inference using new low-power parallel neuromorphic hardware, where belief propagation updates are done using leaky integrate and fire neurons in parallel, while consuming less than 1 W of power. In outdoor robotic experiments, our algorithm was able to consistently produce clean, accurate obstacle maps which allowed our robot to avoid a wide variety of obstacles, including trees, poles and fences. I.
Efficient Estimation for Autonomous Multi-Rotor Helicopters Operating in Unknown, Indoor Environments
, 2012
"... This Dissertation is brought to you for free and open access by BYU ScholarsArchive. It has been accepted for inclusion in All Theses and Dissertations ..."
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This Dissertation is brought to you for free and open access by BYU ScholarsArchive. It has been accepted for inclusion in All Theses and Dissertations