This paper presents a vision-based localization and mapping algorithm developed for an unmanned aerial vehicle (UAV) that can operate in a riverine environment. Our algorithm estimates the three-dimensional positions of point features along a river and the pose of the UAV. By detecting features surrounding a river and the corresponding reflections on the water's surface, we can exploit multiple-view geometry to enhance the observability of the estimation system. We use a robot-centric mapping framework to further improve the observability of the estimation system while reducing the computational burden. We analyze the performance of the proposed algorithm with numerical simulations and demonstrate its effectiveness through experiments with data from Crystal Lake Park in Urbana, Illinois. We also draw a comparison to existing approaches. Our experimental platform is equipped with a lightweight monocular camera, an inertial measurement unit, a magnetometer, an altimeter, and an onboard computer. To our knowledge, this is the first result that exploits the reflections of features in a riverine environment for localization and mapping. C 2015 Wiley Periodicals, Inc.
This paper presents a new method to estimate the range and bearing of landmarks and solve the simultaneous localization and mapping (SLAM) problem. The proposed ranging and SLAM algorithms have application to a micro aerial vehicle (MAV) flying through riverine environments which occasionally involve heavy foliage and forest canopy. Monocular vision navigation has merits in MAV applications since it is lightweight and provides abundant visual cues of the environment in comparison to other ranging methods. In this paper, we suggest a monocular vision strategy incorporating image segmentation and epipolar geometry to extend the capability of the ranging method to unknown outdoor environments. The validity of our proposed method is verified through experiments in a river-like environment.
This paper presents an inertial-aided vision-based localization and mapping algorithm for an unmanned aerial vehicle (UAV) that can operate in a GPS-denied riverine environment. We take vision measurements from the features surrounding the river and their corresponding points reflected in the river. We apply a robot-centric mapping framework to let the uncertainty of the features be referenced to the UAV body frame and estimate the 3D positions of point features while estimating the location of the UAV. We demonstrate the localization and mapping results with sensors on our quadcopter UAV platform in the University of Illinois at Urbana Champaign Boneyard Creek. The UAV is equipped with a light weight monocular camera, an inertial measurement unit (IMU) which contains a magnetometer, an ultrasound altimeter, and an on-board computer. To our knowledge, we report the first result of performing localization and mapping by exploiting multiple views with reflections of features in a river-like environment.
In this paper, we present an omnidirectionalvision-based localization and mapping system which can detect whether a robotic mower is contained in a permitted area. We exploit a robot-centric mapping framework that exploits a differential equation of motion of the landmarks, which are referenced with respect to the robot body frame. The estimator in our system generates a 3D point-based map with landmarks. Concurrently, the estimator defines a boundary of the mowing area with the estimated trajectory of the mower. The estimated boundary and the landmark map are provided for the estimation of the mowing location and for the containment detection. We validate the effectiveness of our system through numerical simulations and present the results of the outdoor experiment that we conducted with our robotic mower.
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