# Tag Info

6

I realize this question already has an accepted answer, but I'd like to provide some additional input. The question of sensor fusion is a good one, but, depending on the application, you don't typically want to "convert" (i.e., integrate twice) the IMU to obtain xyz position. Frankly, in my experience, the best way to approach fusing GPS and IMU data for a ...

3

I believe it's because you're essentially constructing an exponential distribution which has the form Because your loss function will always be >= 0, you form a valid PDF (valid in that it integrates to 1, but your loss function might not make that practically true)

2

I would say that in order to learn C++ up to an acceptable level there is no shortcut: you learn it by using it. And more often than not you learn it by using it together with others that know more than you. Then, I would assess three things: Are there C++ projects in my domain of expertise to which I’d like to contribute? This could be open-source ones, or ...

2

It seem that your are missing some intuition about the function of a Kalman filter type filtering method. To a large degree the working principle of the Kalman filter is combining information from different sources in order to create estimates greater than the measurements from either individual sensor. The advantage of the Kalman filter is that it offers a ...

2

The measurements don't get inserted into $H$. The $H$ matrix is the "measurement matrix" or "output matrix" such that you get an estimate of the output when you multiply $H$ by your state vector estimate $\hat{X}$. You can see this in equation (23) in the paper you linked, on document page 7: The model can be expressed as follows: $$X_{... 2 It is always guaranteed to be positive semi definite. That being said you have to somewhat deliberately set up your system to be that way. So essentially yes it is always positive definite. Reasoning: Covariance matrix by definition is always positive semidefinite. A^TA is always positive semidefinite (Takes care of the propagation matrix and the ... 2 What does the absolute scale mean? In this context, scale refers to what property related to an image? Essentially scale refers to the size of the object/scene that the camera sees. As a projective sensor the camera can't know the depth/size of the object it is viewing. Look at the below image. While we might know that the big and small boxes are different ... 2 This project does exactly that on an RC car. The author is a top competitor in the DIYRobocars community; it's the blue car in this video. He uses tachometry from the brushless motor, an IMU and visual odometry for localization. I don't know the code well enough to point you to any specific file. 2 To complement what Octopuscabbage correctly reported, there exists a strong theoretical foundation for using normal probability distributions in many different contexts, which builds on the Central Limit Theorem (CLT) that explains how the "exponential" distribution can work well with problems involving other types of distributions. As a result, ... 2 Probably the easiest way to do this would be to convert from Quaternion to Roll-Pitch-Yaw rotations, and then your heading is the Yaw angle. I'll note that the Yaw angle is not fixed/correct unless your IMU has a magnetometer. Accelerometers can fix roll/pitch by detecting the gravity/down vector, but North only comes from the magnetometer. You can get more ... 1 It seems that since your "measurements" are just deltas in the state, they should really be considered your control inputs. Intuitively, measurements are supposed to reduce the variance in your state estimate, and controls inputs will increase it. So to me it sounds like you have a problem with only control inputs (deltas to the state) and no ... 1 This might be "normal", depending on how the signal is acquired. Whenever a signal is derived, timing of the signal acquisition is crucial. Counting ticks is not time sensitive, as it does not matter if one tick is acquires a few microseconds later or earlier, what matter is, that it is acquired. For deriving a velocity from the ticks of an encoder,... 1 As you pointed out, y=f\left(u\right) is a static map, hence it does not represent in any way the temporal evolution of a dynamical system. With this in mind, resorting to an observer is fundamentally a wrong approach. An observer, in fact, provides you with an estimate of the temporal evolution of the dynamical system under subject; however, here f\left(\... 1 I don't know if your confusion is with applying the transform to the points or applying it to the pose. So I'll just show you both. The easiest way is to store your points and transform in the homogenous form. For 2D the transform is a matrix(3x3) that looks like$$T=\begin{bmatrix} cos(\theta) & -sin(\theta) & t_x\\ sin(\theta) & cos(\theta) &...

1

As mentioned by @long-smith the standard solution is to use quaternions. However if you specifically are asking how to deal with errors using angles that are modulo $2\pi$ you are going to want to add logic to compute the smallest angle between two angles which take into account wrapping. An example is shorted_angular_distance from the ROS angles package ...

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Yes. Example of this can be found here. Depending on how good your modeling is you could also use the IMU to help detect wheel slippage.

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Indeed position, velocity and acceleration (but also the unit quaternion and the angular velocity of the gyroscope) are related to each other. But the word "biases" refers to the measurement of these quantities, for example the gyroscope will measure the angular velocity plus a bias. Including these biases in the state space allows you the estimate the ...

1

Usually the magnetometer is used to find the yaw. It acts as a digital compass in this case. To calculate roll and pitch you need an accelerometer. But there are some techniques that can be used to calculate the roll and pitch using the magnetometer. For that you need to place a magnet close to the mobile phone and observe the sensor values. Using these ...

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there is some error in your matrix I think. P(k+1)=V(k) And V(k+1)=A(k) so I don't kwon what is the A(k+1) maybe the jerk(jolt) of the vehicle.

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If you can write the dynamics with a matrix, which you have, then a normal kalman filter will be best. However, your measurements will probably be nonlinear. You will find that you won't be able to write your measurements equations using matrices. You will almost certainly need an extended kalman filter because your measurements will be nonlinear.

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I am assuming that by real time path planning, you mean starting off in a partially known environment and updating your 'plan' as you gain more knowledge through your SLAM algorithm. For a real world scenario, two of the biggest concerns here would be a) taking into account new information from the sensors to update your obstacle map and plan, b) being ...

1

DSO initializes the scene and camera poses with a specific scale factor such that the average inverse depth of the pointHessians is one. After the initialization the first two frameHessians are led into the backend to do a bundle adjustment like optimization in which, however, the previous determined scale can change (because the absolute scale is not ...

1

Typically, when doing this type of pose estimation, the essential matrix would be wrapped inside of RANSAC. i.e., you would have a lot of candidate point correspondences and would randomly sample 5 of them to evaluate with Nister's algorithm to give potential pose solutions, do this several times, and pick the best. If there is such a random process involved,...

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