Monocular Vision based Navigation in GPS-Denied Riverine Environments

Yang, Junho; Rao, D. V. Subba; Chung, Soon‐Jo; Hutchinson, Seth

doi:10.2514/6.2011-1403

Cited by 13 publications

(13 citation statements)

References 15 publications

(10 reference statements)

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“…While these platforms could be more practical in simple riverine environments, we aim to develop a platform that can perform in the most difficult situations such as rapidly flowing water, obstructed waterways, or dense forest canopies. Preliminary work in river mapping on small rotorcraft using passive vision and ultrasonic ranging (for elevation estimation) has been reported over short distances (Yang et al 2011). Our work is similarly motivated but we explicitly consider substantially longer missions in which it is important to not only map the extent of the river but also to map the vegetation along the shore line and avoid obstacles that might appear in the middle of the river.…”

Section: Related Workmentioning

confidence: 99%

River mapping from a flying robot: state estimation, river detection, and obstacle mapping

et al. 2012

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Accurately mapping the course and vegetation along a river is challenging, since overhanging trees block GPS at ground level and occlude the shore line when viewed from higher altitudes. We present a multimodal perception system for the active exploration and mapping of a river from a small rotorcraft. We describe three key components that use computer vision, laser scanning, inertial sensing and intermittant GPS to estimate the motion of the rotorcraft, detect the river without a prior map, and create a 3D map of the riverine environment. Our hardware and software approach is cognizant of the need to perform multi-kilometer missions below tree level with size, weight and power constraints. We present experimental results along a 2 km loop of river using a surrogate perception payload. Overall we can build an S. Scherer ( ) · S. Achar · H. Cover · A. Chambers · S. Nuske · accurate 3D obstacle map and a 2D map of the river course and width from light onboard sensing.

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Section: Related Workmentioning

confidence: 99%

River mapping from a flying robot: state estimation, river detection, and obstacle mapping

et al. 2012

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“…Autonomous flight in a riverine environment has also been demonstrated recently (Jain et al., ). Our prior work (Yang, Rao, Chung, & Hutchinson, ) presents a monocular vision‐based SLAM algorithm that uses a planar ground assumption for riverine environments. In Leedekerken, Fallon, & Leonard (), a submapping approach is adopted to address the SLAM problem with an autonomous surface‐craft that builds a map above and below the water's surface.…”

Section: Related Workmentioning

confidence: 99%

Vision‐based Localization and Robot‐centric Mapping in Riverine Environments

Yang

Dani

Chung

et al. 2015

Journal of Field Robotics

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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.

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“…A sonar was used for subsurface mapping, and LIDAR, camera, and radar sensors were used for terrestrial mapping to account for degradation of GPS. In [10], a vision-based SLAM algorithm exploiting planar ground assumption on the border of the water and the ground was presented for localization and mapping in riverine environments. In [11], an on-board vision-based river detection and navigation algorithm was developed for an autonomous UAV to monitor a river from high altitude.…”

Section: Related Workmentioning

confidence: 99%

Inertial-Aided Vision-Based Localization and Mapping in a Riverine Environment with Reflection Measurements

Yang

Dani

Chung

et al. 2013

AIAA Guidance, Navigation, and Control (GNC) Conference

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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.

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Monocular Vision based Navigation in GPS-Denied Riverine Environments

Cited by 13 publications

References 15 publications

River mapping from a flying robot: state estimation, river detection, and obstacle mapping

River mapping from a flying robot: state estimation, river detection, and obstacle mapping

Vision‐based Localization and Robot‐centric Mapping in Riverine Environments

Inertial-Aided Vision-Based Localization and Mapping in a Riverine Environment with Reflection Measurements

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