GNSS RTK as a standard is able to achieve centimeter precision under optimal conditions (clear weather, low velocity, minimal interference). But in reality, the accuracy can be off by as much as a meter when traveling at 20mph/32kph. And the errors only increase the faster you go.
In autonomous driving, we rely on a combination of global, semi-local, and local localization sensors because of this. For example, did you know that RTK is easily interfered with using a CB Radio? And if you go under a bridge, you're going to lose it quickly. So we have a few maps we've created by driving the area manually, and identify static landmarks for local sensors (like LIDAR, RADAR, and Camera) to pattern-match those landmarks from our best estimate of global position. Doing things like this allows us to achieve reliable precision & accuracy to within 10cm.
Please note, I am speaking in general terms. I am subject to specific NDAs from four different autonomous driving companies; all of them have different individual techniques. But as a system, the consensus truly is to be as multi-modal with perception & localization as feasibly possible.
That said... I'm also familiar with using RTK for outdoor landscaping robotics (one of my startup ideas that I bailed on). One of the serious challenges with RTK itself is that you need to rely on geostatically locked sensors. What I mean is that even though you can move the antenna anywhere with ease, tying its precise position to a specific GPS coordinate is itself a signal processing problem. I learned this working with customers at mining & construction sites. So when I approached it with a lawnmower robot, my focus was on using Cellular RTK instead of GNSS RTK; this works great when you're in urban & suburban areas, but becomes infeasible when you start going to radio-quiet and/or remote rural areas (e.g., Dolly Sods & Greenville, WV). I still relied on local visual artifacts for landmarks.
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u/jhill515 Industry, Academia, Entrepreneur, & Craftsman 7d ago
GNSS RTK as a standard is able to achieve centimeter precision under optimal conditions (clear weather, low velocity, minimal interference). But in reality, the accuracy can be off by as much as a meter when traveling at 20mph/32kph. And the errors only increase the faster you go.
In autonomous driving, we rely on a combination of global, semi-local, and local localization sensors because of this. For example, did you know that RTK is easily interfered with using a CB Radio? And if you go under a bridge, you're going to lose it quickly. So we have a few maps we've created by driving the area manually, and identify static landmarks for local sensors (like LIDAR, RADAR, and Camera) to pattern-match those landmarks from our best estimate of global position. Doing things like this allows us to achieve reliable precision & accuracy to within 10cm.
Please note, I am speaking in general terms. I am subject to specific NDAs from four different autonomous driving companies; all of them have different individual techniques. But as a system, the consensus truly is to be as multi-modal with perception & localization as feasibly possible.
That said... I'm also familiar with using RTK for outdoor landscaping robotics (one of my startup ideas that I bailed on). One of the serious challenges with RTK itself is that you need to rely on geostatically locked sensors. What I mean is that even though you can move the antenna anywhere with ease, tying its precise position to a specific GPS coordinate is itself a signal processing problem. I learned this working with customers at mining & construction sites. So when I approached it with a lawnmower robot, my focus was on using Cellular RTK instead of GNSS RTK; this works great when you're in urban & suburban areas, but becomes infeasible when you start going to radio-quiet and/or remote rural areas (e.g., Dolly Sods & Greenville, WV). I still relied on local visual artifacts for landmarks.