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Abstract
This article presents an enhanced version of the Monte Carlo localization algorithm, commonly used for robot navigation in indoor environments, which is suitable for aerial robots moving in a three-dimentional environment and makes use of a combination of measurements from an Red,Green,Blue-Depth (RGB-D) sensor, distances to several radio-tags placed in the environment, and an inertial measurement unit. The approach is demonstrated with an unmanned aerial vehicle flying for 10 min indoors and validated with a very precise motion tracking system. The approach has been implemented using the robot operating system framework and works smoothly on a regular i7 computer, leaving plenty of computational capacity for other navigation tasks such as motion planning or control.
Highlights
The new manufacturing paradigm pursues the acceleration of delivery rates through the gradual implementation of automated processes
The main contribution of this work focuses on the combination of all three: an Monte Carlo localization (MCL) algorithm relying on a previously built 3-D map, an RGB-D sensor for odometry and point cloud matching, and radio-based sensors installed in the environment and localized within the map
The use of GPUs is discarded; we propose an approach that can be implemented in standard CPUs and not limited its applicability in regular platforms
Summary
The new manufacturing paradigm pursues the acceleration of delivery rates through the gradual implementation of automated processes. Outdoor operation for aerial robots based on global positioning system (GPS) can be generally assumed due to the existence of low-cost commercial products offering mature performances in a wide variety of applications.[5,6,7] Despite this, autonomous aerial vehicles are starting to play a major role in applications such as inspection,[8] search and rescue,[9] or security surveillance,[10] which usually require the aerial robots to fly in dense environments, at low altitudes or indoors. The update frequency of GPS location queries is not enough to achieve the aforementioned tasks in a robust and safe manner, since the aerial robots are supposed to be operating in small environments and move at relatively high speeds
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