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Autonomous Robotic Mapping
Does anyone know of any way to create an accurate mapping system for a small area outdoor autonomous robot? I was thinking GPS but they can be off by 3 meters. Then I was thinking some sort of triangulation localization technique but I figured Id put it out there to see if anyone has any ideas.
Thanks, mreda |
Re: Autonomous Robotic Mapping
Perhaps this might be of interest to you https://github.com/JacisNonsense/Pathfinder
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Some variables to consider: - You said that 3m is too much error, but what sort of resolution are you looking for? - How large of an area are you dealing with? 10s of meters, 100s of meters, 1000s of meters? - Is it a fixed workspace (i.e. you can set up beacons) or are you moving into an unknown territory? - What's the line-of-sight visibility like? Is it a fairly open space, or are there frequently objects which can occlude the robot's sensors? If there are occluders, are they large and metallic (i.e. would block radio frequencies)? - How much money do you want to spend? If it's an unknown space, you're going to need something like SLAM. There are many prebuilt libraries available for this (check out some of the ones that are available through ROS for example). If you have a workspace that you control, here's a few solutions for several settings of the above parameters: - Use a single camera and use the position and apparent size of a target(s) to determine 3d pose. This is how most FIRST vision systems work, and how the Oculus Rift tracking camera works. For a fairly out-of-the-box solution, take a look at some of the fiducial tracking libraries, such as AprilTags. - Use several different cameras and track a target(s) and triangulate the pose. This is how motion capture cameras work. - Use a Microsoft Kinect or other RGBD camera - One of the magnetic position sensing systems from Sixense - Use one of the UWB (Ultra-wideband radio-frequency) localization systems. I have one of the kits from Decawave sitting on my desk waiting for when I have enough time to play with it more. - Use a differential GPS system. - Place a whole bunch of RFID tags with unique IDs around the space and use an RFID reader to scan the nearest tag to determine your position. - Mount several string pots to your robot and connect the ends to various points around the space and trilaterate your position. As long as your workspace is very open, you have an instant, high accuracy estimate of your robot's position with very little processing required. |
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Another method I've heard used is to point a camera at the ceiling and use identifiable features on the ceiling to localize. QR codes on the ceiling would make that even easier. |
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I think your best bet might be to place visible beacons in known locations around the area, with a 360 degree camera view tracking the direction to each beacon in order to do inverse triangulation. |
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I used to work at a company that provided SLAM solutions out of the box to potential PHD's. We'd deliver anything from the top end systems ($70k+) to the quite budget friendly options.
If you're able to share your budget constraints, it would greatly help us recommending tech to get you where you need to be. |
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shouldn't need to spend too much money on a SLAM solution. and yes SLAM works outdoors
Last I checked you could get an xbox 360 kinect used for 25 a pop at gamestop. As long as whatever you are running can run ros on ubuntu you should be good. Odroid makes good cheap single board computers, and while I don't have any experience with the jetson boards, they should also be rather good, if not a bit on the expensive side going by what I've read on CD. |
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You may be able to find a cheaper option, but with 5-10 minutes of searching, the cheapest 360* camera that supports live streaming to a computer that I could find is the $449 VSN MOBIL V.360° with a $299 HDMI converter. If you want 360* coverage, another certainly cheaper option is to get several webcams pointed in different directions - the downside to this approach is you have to do all the extrinsic camera calibration yourself, which is generally a pain. I might recommend starting with just a single camera with a wide angle lens (be aware that you'll have to correct for distortion). You can use one of the existing visual fiducial tracking libraries. I mentioned AprilTags already; ARToolKit is also widely-used. If you have a good view of the fiducial marker tag (place lots of tags around the area so the robot always has a good view of at least one), these libraries will give you a 6D pose estimate of the tag relative to the camera. You can invert this to give you the position of the robot relative to the tag, which then gives you the absolute pose of the robot when you add it to the known position and orientation of the tag. Once you have position estimates derived from the individual observed beacons, you can fuse them to create a more accurate position estimate for the robot. There are fancier methods available, but a 90% solution could probably be achieved with a Kalman filter and a few heuristics for resetting. Here's a couple of projects that claim to do similar to what you're asking (I haven't tried them personally): - https://github.com/ProjectArtemis/aprilslam - http://pharos.ece.utexas.edu/wiki/in...SimonSays_Demo - https://github.com/LofaroLabs/POLARIS / http://wiki.lofarolabs.com/index.php..._Indoor_System |
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