# Tag Info

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Precise vehicle location is not a very useful piece of data in autonomous driving so I would not expect to see many products focusing on it. Path planning and following for cars is a solved problem. If you have a static environment where nothing changes, any good position sensing will let you control a vehicle to follow a path. However, there are very few ...

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For high cost and high accuracy, you can use motion capture systems such as https://www.vicon.com/, https://optitrack.com/, or http://phasespace.com/. These systems will typically have a dedicated (Windows) computer driving the cameras. You will have some code to write to pull this data out and transmit to the robot (if that is where you want it). On the ...

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I think that the methods which require no modification to the environment--e.g. gmapping with 2D lidar or ORB-slam with camera--are probably too computationally intense it to run on the pi alone. You would need to run a separate server and connect over Wi-Fi. This approach works, and I've actually gotten it to work over the internet instead of just a local ...

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You are essentially making your own IMU. The way this works is essentially: the accelerometer gives you linear acceleration the rotational gyro gives you angular velocity integrate the rotational gyro over time to give you angular position integrate the accelerometer over time to give you linear velocity double integrate the accelerometer over time to ...

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Basically you can utitilize one global frame for both robots since each robot has its own SLAM. SLAM provides an estimate for a robot's pose (i.e. location and direction). If you unify the global frame for both SLAMs, then you can determine the poses of the two robots. I've drawn a picture to illustrate my approach. As you can see from the above picture, ...

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These values are relative to the camera. Z is always positive as the camera can't see what is behind. X and Y can be positive or negative depending on if an object is left/right or higher/lower than the camera's viewing direction.

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I just wanted add on to both user12895 and AL-ROBOT's answers. Based on experience: What you need is an (Iterative Point Cloud) ICP algorithm. Do not worry, if the robot cannot detect the entire map when it scans with the lidar, you can just match what ever data you capture to a part of the map depending on your current position. Great work on getting ...

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1- Of course you don't have perfect matches 2- ICP is not used for localization, it is used to calculate the transformation the robot's pose has undergone (T+R) 3- To localize your robot, you would have to use a particle filter as lucab has said

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Let's suppose you have a required (continuous) trajectory in the form of an equation that you want to follow, for example take a straight line (whose equation is y=2x+5), you can break it down into discrete points and generate a velocity profile out of this trajectory, the calculated velocity profile could be fed to inverse kinematic equations [1] of robot (...

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The problem you are describing is quite similar to line following from a controls perspective, there is a difference in how the line is detected. Line following robots use a wide variety of ways to detect line. Simplest is probably photo-resistors or phototransistors, however magnetic stripes (lines) and hall effect sensors have been quite popular in the ...

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I can't use any line-following method. Actually, you are quite wrong, the way I understand the problem. It is a line-following. It is just that the line is not painted (like on the road). The line is the edge of the table. The way I see it, your robot needs to look up, and detect the line "painted" by the edge of the table on the ceiling. Once the ...

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We developed this for pipeline inspections and used this to map a pipeline. We found magnetism not to be very reliable and did it without. It works, but you have accumulative errors that can be significant. In our case you will try to find reference points and make corrections for these accordingly. And with a pipeline you always have 2 reference points ...

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The short answer is that it's possible, but tricky. To estimate position you integrate accelerometer readings over time to get linear velocity estimates, and then integrate the velocities to get position estimates. The downside of this double integration is that either initial readings must be very accurate, or sophisticated smoothing / filtering ...

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This isn't something that can just be answered. There are numerous ways to do this. Low Tech: You can go low tech with IR emitters and retroflective tape (or retroflectors) with IR sensors. High Tech: A more high-tech solution would be to utilize ultra-wide band transceivers. With UWB you can pinpoint your rovers in 3 dimensional space around each other. ...

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The amount of rotation that the motor is capable of depends on the physical design of the motor. Usually the motor is combined with a transmission (eg gears) of some kind which also influence how far the combination (called an Actuator) can move. Many motors, and many actuators, can rotate infinitely, without limit. The minimal angle that one can reliably ...

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I am not sure what you mean by 'displacement value which is acceleration independent'. The conversion of relative positioning you're getting from /dev/input/mouse will always depend on the DPI of the mouse. All you'd have to do is figure out the DPI (either from the specs or from measurements, and hence the conversion between reported dx/dy values, and then ...

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The XYZ readings from the camera are in the reference frame of the camera. @FooBar is correct about the X/Y values: they are planar about the center of the camera, just like the OpenGL viewing window. I don't know the maximum range of the point-cloud data, but my suspicion is that the maximum z value is 1. (This could change, however, depending if you have a ...

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Yes it might be possible to obtain the location of the robot, if you use multiple Bluetooth nodes(preferably 4 excluding the one in the robots for a free space) and use the rssi values from the Bluetooth nodes to triangulate the position. But still the output is susceptible to high covariance due to some environmental factors, so for a more accurate ...

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For localization problems, you will always need to have some references to look at (using only the IMUs of the phone will lead in error as time increases). For example, GPS uses the relative position of the satellites, LIDAR SLAM uses the relative position of features, Wi-Fi positioning systems use the relative position of the access points. In your case,...

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I am not sure how you are driving your stepper motor. In case you developed your own way, you could connect an analog pin to the wires that drive the stepper motor. These should be able to detect an induced voltage if the stepper is turned without being driven.

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The problems of outdoor-tracking are described in http://www.tinmith.net/papers/piekarski-ismar-2006.pdf page 2. As a solution was named the WearTrack System which worked by ultrasonic technology. The system tracks the hand relative to the head of the user. Other hardware solutions are presented here https://www.evl.uic.edu/jelias1/vr-shapetape.pdf (...

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Unless you want to put this product at the market (which I doubt), then I wouldn't hope for an RTK-GPS pair to solve your problem. It could help you, in the extreme case of a dense road network, if a simple GPS receiver got such a bad reading that it would place in in the next block. But it is unlikely that you will perform in such an environment. Instead, ...

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First of all you should start to check 2 things: the angle $\alpha$ is given by some sensor the object and the path to the object (blue dotted line) both lie in the arm workspace If 1. is not met, I suggest you to look in the literature of eye-in-hand servoing. If 2. is not met your object can not be reached ... you might still want to travel until the ...

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Using the commanded speed in the prediction update of a Kalman filter which estimates speed and higher order states can be entirely reasonable. In situations where the time constant of the controller is slow, you will gain a considerable amount state estimate accuracy by estimating state derivatives using the control loop dynamics. In situations where the ...

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This is another aspect of a longer answer I wrote here. Briefly restated, your problem is that a wheeled robot like this is nonholonomic, which means you can just use absolute encoder counts and get a valid result for position and heading. You have two axes of control: a left wheel and a right wheel; and you have three degrees of freedom: left/right ...

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The suggestion posted by N. Staub worked well for me, I was able to perform the hack onto the HS-422 where the min and max readings of the servo potentiometer were [0.34-2.04 Vdc] and by using Arduino MEGA 2560, which has the option to provide an interval voltage reference of 2.56V, as fellows: analogReference(INTERNAL2V56) #Arduino MEGA Only With this ...

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I think from the spec sheet, it means that at some configurations, J3 could actually reach +255$^\circ$. For example, when J2 = -100$^\circ$, J3 may be able to reach that maximum of 255$^\circ$. In other words, the range of J3, for example, depends on a specific value of J2. To find out the exact range of J3 given a specific value of J2, you may need a 3D ...

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Controlling a Servo from a PC is more than just generating a PWM signal The indication that you want to control the PWM signal for the Servo from a PC implies that your want to close the servo control loop yourself. I have done that before and it is VERY HARD (it took us several thousand man hours to create). Much of the code has to reside in the Windows ...

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Take a bunch of measurements of your system while states are static and compute the noise matrix yourself. As long as recording measurements is relatively straightforward this should be a painless process. It will also verify that your measurements are as accurate as you believe.

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