Inside Unmanned Systems

DEC 2018 - JAN 2019

Inside Unmanned Systems provides actionable business intelligence to decision-makers and influencers operating within the global UAS community. Features include analysis of key technologies, policy/regulatory developments and new product design.

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37 December 2018/January 2019  unmanned systems inside Various sensors work together to make these fea- tures possible. Lamprecht went over the pros and cons of these systems and sensors, which include: COMPUTE SOLUTIONS. Such solutions make it pos- sible to work with data from different sensor types, Lamprecht said. For development purposes, an indus- trial, ruggedized computer is used in parallel with the Robot Operation System (ROS). ROS is a library of tools that makes it possible to take information from different sensors to program control of a robot, in this case a car. The system has a built-in visualization tool that allows users to see what the sensor sees. "Users can monitor the behavior of the algorithms they've written," he said. "They can see in real time how software is behaving and make modifications to that." RADAR. This sensor has been used in production ve- hicles for more than a decade with applications that include forward collision warning and blind spot monitoring. Radar provides robust measurements and works in adverse weather conditions such as fog, rain and snow. The trade-off with this sensor is distance accuracy. A larger bandwidth will allow expanded radar capabilities, Lamprecht said. New systems also will include vertical resolution so the sensor can provide a 3-D snapshot instead of a 2-D measurement. LiDAR. This sensor is typically used as a redundancy source to check against cameras or radar, Lamprecht said. LiDAR provides fast resolution at a fast update rate, but can be expensive. "We're waiting for solid state LiDAR and MEMS technology to develop a more robust sensor that can be embedded into car bumpers or grills," he said. "That is more appealing than spinning a mechani- cal LiDAR on top of the vehicle. Manufacturers are also trying to refine the form and function of what LiDAR will look like." VISION SENSORS. A low-cost technology, cameras are al- ready being used by automotive manufacturers as the primary sensor for obstacle classification, Lamprecht said. Cameras make it easy to tell if an object is a car or a pedestrian, for example. They're also good for mod- eling lanes on a road. The downside is the excessive amount of data the raw images produce. It can be dif- ficult for computers to process it all, as well as store it. WHAT ATTENDEES WANTED TO KNOW Participants had the chance to ask questions during the webinar. Here are a few of those questions: •  What is the effect of jamming and spoofing in integrity monitoring? How can it be predicted? •  Will dual frequency chips eliminate the need for the broadcast ionospheric corrections mentioned? •  What are some of the proposed alternatives to GNSS in GNSS-denied environments (tunnels, urban canyons, underground parkades)? •  How soon will we have usable PPP receivers? •  Other than the initial convergence time of a couple of minutes with TerraStar, what is the re-convergence time after a satellite loses lock in an urban area? •  Can you comment on how the aviation approach to integrity can be adapted to automotive safety for cases where GNSS is integrated with other sensors (such as vision and LiDARs) to maintain required performance in degraded environments (e.g. dense urban). For example, multiple faults are generally present in vision data and RAIM-type of algorithms are not scalable. FOR MORE INFORMATION •  Visit and •  Visit TO WATCH THE FULL WEBINAR, GO TO: https://attendee. Samer Khanafseh a research assistant professor at Illinois Institute of Technology (IIT), Chicago, and co-founder and Manager of TruNav LLC Lance de Groot Senior Team Lead, Geomatics Software Safety Critical Systems, NovAtel Demoz Gebre-Egziabher Professor, Aerospace Engineering & Mechanics, University of Minnesota Terry Lamprecht Director of Products, AutonomouStuff THE PANELISTS

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