Editorial Feature

3D Drone Imaging to Go Through Walls

Drone technology has undergone an impressive expansion in its applications over recent years, as this once previously exclusive technology for military purposes has found a thriving purpose in industries including agriculture, entertainment, surveillance, construction and much more.

Drones that are capable of capturing high resolution images through the barrier for walls of important information, such as that which is required during urgent security, disaster management and search and rescue situations, has brought a life-saving aspect to this technology.

By combining the use of Radio Frequency (RF) sensing signals with drone technology, a group of University of California (UC) Santa Barbara Researchers have developed an approach that provides high quality 3D images through walls by wireless power measurement.

Based on the early work conducted by UC Santa Barbara Researchers Saandeep Depatla, Lucas Bucland and Yasamin Mostofi who successfully utilized WiFi RSSI measurements to capture a two dimensional (2D) image through walls, the current team looked to drone technology to enhance the imaging process for 3D purposes.

In addition to equipping the UAVs with wireless technology, the team based their approach on Markov random field modeling, loopy belief propagation and sparse signal processing in order to adequately design their project. In their experiment, the group of researchers led by Yasamin Mostofi used two 3DR X8 octocopters, both of which were equipped with an on-board Pixhawk module to control the flight of the UAV, as well as transfer received information about the flight to the monitoring computer system.

To provide precise localization information to the Researchers, they mounted a Google Tango Tablet, which is equipped with its own set of cameras and sensors, onto each octocopter. While the Researchers provided the octocopter with specific way-points to reach during their flight, which is controlled by the Pixhawk, these unmanned air vehicles (UAVs) were completely autonomous during the course of their flight route.

One of the octocopters, given the name ‘TX UAV,’ was mounted with a WiFi router, as it was responsible for transmitting the WiFi signal, whereas the role of the WLAN card-equipped ‘RX UAV’ was to receive the signal. Each octocopter was responsible for imaging an unknown area whose dimensions were 2.96 m x 2.96 m x 0.4 m for the two cube scenario, and 2.96 m x 2.96 m x 0.5 m for the L-shape scenario.

To understand the precision of both the TX UAV and the RX UAV, a percentage ratio that takes the total number of measurements taken by the drone to the total number of unknowns in the discretized space was created. From the two-cube scenario, the UAVs produced an image based on 3.84% measurements, whereas the L-shaped scenario showed 3.6% measurements.  

The work conducted by the group of Researchers in this study provided an important insight on how 3D imaging is possible by combining UAV technology with transmissible wifi. By creating this intersection between robotics and communications technology, the Researchers achieved high resolution imaging in an area that was equipped with strictly only wifi signals.

In an effort to combat the problem of taking images through walls while simultaneously manipulating RF signals to enhance the accuracy, precision and production of the 3D images, this team of UC Santa Barbara Researchers are hopeful that they have set a foundation for electromagnetic, signal processing, networking and similar optical industries to build upon for their future endeavors.

The team of Researchers hope to employ more advanced localization technologies into their future UAV projects that are capable of taking potentially impactful environmental factors such as harsh winds, lightening and other adverse weather conditions and eliminating their affects on image quality.

References:

  1. “3D Through-Wall Imaging with Unmanned Aerial Vehicles Using WiFi” C. Karanam, Y. Mostofi. Association for Computing Machinery. (2017). DOI: 10.1145/3055031.3055084.

Image Credit:

elywnn/ Shutterstock.com

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