The Autonomous Robots Lab (ARL) at the University of Nevada, Reno (UNR) was recently awarded a project on “Mine Inspection Robotics”. Funding of the project comes from the Nevada Knowledge Fund, while it is further supported and strongly based on the collaboration with Barrick Gold Corporation. The emphasis of the project relates to the utilization of aerial robots for underground mine inspection, mapping and monitoring. A more thorough and formal announcement will be made soon.
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A new video presents ongoing and updated results in the field of electric bus instrumented navigation. The following papers of our lab were just accepted at the IEEE Aerospace Conference
Our group recently published the paper:
This video presents results on autonomous exploration and simultaneous object search through semantically-enhanced path planning for aerial robots. This video presents results on autonomous exploration and mapping of tunnel environments using the developed Multi-Modal Mapping Unit (M3U). The M3U tightly synchronized a stereo camera pair with an Inertial Measurement Unit and super-bright flashing LEDs, while it further fuses time-of-flight 3D depth sensors. It allows for GPS-denied localization and mapping visually-degraded environments, an ability that is then combined with autonomous exploration path planning. The Autonomous Robots Lab provided a series of talks during September and October and specifically:
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Prof Dr. Marco Hutter of the Robotic Systems Lab of ETH Zurich gave a talk on legged robotics and mobile manipulation on July 31st. The talk took place at the Knowledge Center, hosted and organized by the Autonomous Robots Lab.
Autonomous Aerial Robotic Exploration and Mapping of a Railroad Tunnel in Degraded Visual Conditions7/26/2017 This video presents indicative results of a sequence of field experiments conducted to verify and evaluate new algorithms and systems for autonomous exploration and mapping of tunnel environments using aerial robots. A multitude of such experiments were conducted to verify different algorithms and perception systems.
The Tunnel 4 of V&T Railroad at Virginia City, Nevada was selected for these tests and refers to an approximately 14ft wide, 25ft tall and 450ft long old railway tunnel. For its autonomous exploration, a small aerial robot equipped with a multi-modal localization and mapping unit, as well as localization uncertainty-aware exploration path planning intelligence was developed and utilized. Given no prior knowledge of its environment, the robot was deployed inside the tunnel and left to explore on its own. The result is a dense map of the tunnel revealing its geometrical and visual characteristics. In addition, a set of further GPS-denied localization and mapping technologies were tested toward the very high-fidelity mapping of the tunnel from all possible perspectives. |
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