Field Testing Visual Odometry: Results from Benchtop to Flight for Autonomous Science Mission Needs

Neilan, James H.; Eddy, Josh; Tran, Loc; Kelley, Benjamin N.; McQuarry, Andrew K.; Vaughan, Matthew; Williams, Richard; Allen, B. Danette

doi:10.2514/6.2017-3989

Cited by 3 publications

(1 citation statement)

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“…With advances in processing power, it became possible to use PTAM onboard a vehicle. Presently, SLAM and PTAM have become well developed enough that they are used on mission-ready vehicles [26] and offered as part of commercially available autopilot packages [33].…”

Section: Vision-based Indoor Navigationmentioning

confidence: 99%

Navigation of Indoor Spaces Using Multiple Quadrotors

Katona

Morgansen

2019

AIAA Scitech 2019 Forum

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The operation of uninhabited aerial vehicles (UAVs) indoors faces many challenges, notably from the ineffectiveness of the sensors typically used on these vehicles for flight in outdoor environments, such as Global Navigation Satellite Systems (GNSS) and magnetometers. However, there are many situations when the ability to use a UAV indoors is desirable, such as for surveying underground infrastructure, performing inventory of a warehouse or assisting in search-and-rescue operations. This thesis addresses three challenges facing the autonomous indoor flight of UAVs. First, it proposes a platform for developing a low-cost UAV system using commercial-off-the-shelf hardware and open-source software. The system consists of one or more quadrotors, an ultrasonic beacon system and a ground control station. Next, it surveys methods for measuring the yaw of a vehicle in an indoor environment, including the magnetometer and computer vision techniques The thesis then proposes a method for using an ultrasonic beacon system to measure yaw. The advantages and disadvantages of each method are noted. Finally, the thesis introduces a technique for coordinating a network of multiple vehicles to provide localization to a vehicle performing a mission by moving the reference beacons of the ultrasonic beacon system throughout the space. The algorithm for choosing waypoints for each vehicle is described, and simulations for the algorithms are presented for multiple cases.

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Section: Vision-based Indoor Navigationmentioning

confidence: 99%