Moving object detection for unconstrained low-altitude aerial videos, a pose-independant detector based on Artificial Flow

Castelli, Thomas; Trémeau, Alain; Konik, Hubert; Dinet, Éric

doi:10.1109/ispa.2015.7306030

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…Our safety planning and navigation scheme can be implemented on-board a UAV and will consist in the following steps: 1-before takeoff, acquire necessary GIS data for the mission area, and generate mission waypoints using global weighted path planning, 2-during the flight, geo-register the embedded camera's images using a sensor model and gimbal readings, detect moving objects (as in [3]) or any other type of objects to avoid, and generate new local path and waypoints to stay clear of the detected objects.…”

Section: Discussionmentioning

confidence: 99%

Autonomous navigation for low-altitude UAVs in urban areas

Castelli,

Sharghi,

Harper

et al. 2016

Preprint

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In recent years, consumer Unmanned Aerial Vehicles have become very popular, everyone can buy and fly a drone without previous experience, which raises concern in regards to regulations and public safety. In this paper, we present a novel approach towards enabling safe operation of such vehicles in urban areas. Our method uses geodetically accurate dataset images with Geographical Information System (GIS) data of road networks and buildings provided by Google Maps, to compute a weighted A* shortest path from start to end locations of a mission. Weights represent the potential risk of injuries for individuals in all categories of land-use, i.e. flying over buildings is considered safer than above roads. We enable safe UAV operation in regards to 1-land-use by computing a static global path dependent on environmental structures, and 2-avoiding flying over moving objects such as cars and pedestrians by dynamically optimizing the path locally during the flight. As all input sources are first geo-registered, pixels and GPS coordinates are equivalent, it therefore allows us to generate an automated and user-friendly mission with GPS waypoints readable by consumer drones' autopilots. We simulated 54 missions and show significant improvement in maximizing UAV's standoff distance to moving objects with a quantified safety parameter over 40 times better than the naive straight line navigation.

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Section: Discussionmentioning

confidence: 99%

Autonomous navigation for low-altitude UAVs in urban areas

Castelli,

Sharghi,

Harper

et al. 2016

Preprint

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“…Vision analysis techniques can provide high-accuracy obstacle representations. Although the high computation overhead has hindered the adoption of this approach in the auto-navigation of indoor mini quadrotors [5], the recent technological advances in the on-boardscale micro-computers, such as Raspberry Pi, Intel Edison, 1 Mr. Shin is a graduate student of the computer science and information technology (CSIT) department at the University of the District of Columbia (UDC), Washington, DC 20008, donghyeok.shin@udc.edu 2 Dr Kim is with Faculty of the CSIT department at UDC, Washington, DC 20008,junwhan.kim@udc.edu 3 Drs Yu, and Jeong are with Clearton, LLC (www.clearton.com),{byu, djeong}@clearton.com and Arduino, have been significant [6], and the distribution of advanced vision analysis techniques in open source libraries, such as OpenCV [7], has been accelerated. In the light of these recent developments, we focus on designing a visionbased method for detecting and avoiding obstacles for realtime autonomous navigation of mini quadrotors in indoor environments with moving obstacles.…”

Section: Introductionmentioning

confidence: 99%

On avoiding Moving Objects for indoor autonomous quadrotors

Shin

Kim²,

Yu³

et al. 2016

2016 IEEE International Conference on Automation Science and Engineering (CASE)

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A mini quadrotor can be used in many applications, such as indoor airborne surveillance, payload delivery, and warehouse monitoring. In these applications, vision-based autonomous navigation is one of the most interesting research topics because precise navigation can be implemented based on vision analysis. However, pixel-based vision analysis approaches require a high-powered computer, which is inappropriate to be attached to a small indoor quadrotor. This paper proposes a method called the Motion-vector-based Moving Objects Detection. This method detects and avoids obstacles using stereo motion vectors instead of individual pixels, thereby substantially reducing the data processing requirement. Although this method can also be used in the avoidance of stationary obstacles by taking into account the ego-motion of the quadrotor, this paper primarily focuses on providing our empirical verification on the real-time avoidance of moving objects.

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