Skip to main content
Share Your Experience: Take the 2024 Developer Survey
10 votes
Accepted

How can we use the accelerometer for altitude estimation?

The barometer carried on the pixhawk has an altitude resolution of 10 cm. If that isn't enough, you could write a kalman filter that uses the accelerometer data in the prediction step and the ...
holmeski's user avatar
  • 1,853
3 votes
Accepted

quaternion implementation

So, as I mentioned in an earlier comment, it looks like you're using a mashup of methods. You're not applying any one method correctly; instead you're mis-using part of one method, then using the ...
Chuck's user avatar
  • 16k
3 votes

Need help regarding development of Extended Kalman Filter for sensor-data fusion of odometry and IMU data

Adding to the above, my favorite way to debug a misbehaving filter is to isolate each step. Make sure your prediction step works before correcting it. Your bot should drive straight right with 0,0,0 ...
Josh Vander Hook's user avatar
3 votes

Will there be any interference when distance sensors facing each other?

The amount of interference highly depends on the sensor type and how you use them. One of the worst for this is probably ultrasonic rangers: These are going out of fashion now, but older robots used ...
Ben's user avatar
  • 5,865
3 votes

How to deal with asynchronous samples in a kalman filter framework multi-sensor fusion?

The correct way of integrating multi-rate observations in a Kalman framework when the measurements are unavailable is to let the system evolve resorting merely to the prediction steps. Therefore, set ...
Ugo Pattacini's user avatar
2 votes

Open source implementations for GPS+IMU sensor fusion?

A followup on holmeski's reply. It seems those pages have been taken down. I found the old webpages on the 'Way Back Machine'. Pixhawk Attitude Estimator (EKF) Pixhawk ekf_att_pos_estimator (...
Awbmilne's user avatar
2 votes

Need help regarding development of Extended Kalman Filter for sensor-data fusion of odometry and IMU data

You should first validate your filter is working before second-guessing your modelling choices. But I agree both those filters look OK (although I did not double check all the maths) and both of your ...
Gouda's user avatar
  • 902
2 votes
Accepted

Robot positioning using IMU quaternion data?

The quaternion only contains information about the rotation of the vehicle. It will not contain information about the location of you vehicle on a 2-d plane. One method of converting quaternions to ...
holmeski's user avatar
  • 1,853
2 votes

Gyro Yaw Drift Compensation With The Aid of Magnetomer

my roll and pitch drifts are corrected with accelerometer inside IMU You mean they're being correct by the IMU, or you're correcting the readings yourself using the accelerometer from the IMU? i ...
Chuck's user avatar
  • 16k
2 votes

Is it possible to track position using gyroscope and accelerometer without a magnetometer?

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 ...
xperroni's user avatar
  • 1,363
2 votes
Accepted

EKF sensor fusion

An EKF or any of the variants of the Kalman filter, as you said mainly works in two steps: prediction and correction. The prediction steps gives you a state estimate based on your process model and ...
Vishnu Prem's user avatar
2 votes

Bias correction for multiple sensor fusion through Kalman Filtering

Your state vector $x$ is correct. When you do bias estimation(look up IMU kalman filters) you assume that the bias is constant/slowly varying. So your $F$ matrix is just the identity(5x5). Your $H$ ...
edwinem's user avatar
  • 1,871
2 votes
Accepted

GPS Course vs IMU Course

So this probably won't work as course over ground and heading are often 2 different things. Heading is the direction your vehicle is facing, while course over ground is the direction your vehicle is ...
edwinem's user avatar
  • 1,871
2 votes

No difference between UKF and EKF for SLAM

The EKF is a first-order approximation, which is achieved by linearizing the system about the current state estimate (i.e., the mean). In some cases, the EKF is not stable due to nonlinearities. For ...
Ralff's user avatar
  • 345
2 votes

How to actually fuse sensor using Extended Kalman Filter

I've never worked on Mecanum wheels before so I researched a bit. One of the first things I look for is there has to be a way to combine all of the encoder velocity measurements. Apparently, Jacobian ...
Masmm's user avatar
  • 21
2 votes
Accepted

How to use knowledge of sensor noise

You are correct. It's just a matter of interpretation. (1) is the guess on the location of the true value whereas (2) is simulating your sensor behavior. Your equation in (1) can be converted to N(z,...
Chanoh Park's user avatar
  • 1,577
2 votes
Accepted

What would be a way to estimate IMU noise covariance matrix?

Usually, IMU manufacturers implement some kind of filter to remove the noise these days, therefore the probability is your IMU is not throwing raw values. Nevertheless, you can initiate the sensor ...
Franky's user avatar
  • 536
2 votes

Merging multiple LIDARs real time

Certainly merging two laser scans into a single point cloud is doable. Here's an example tool and you can easily concatenate said point clouds too. However I'd like to suggest that you reconsider some ...
Tully's user avatar
  • 25.1k
2 votes

Include information on the environment in map-aware particle filter

Answer Create a new "probability grid" for each sensing modality and combine them after you have estimated the position of the robot in each of the many different grids. In short, you have a ...
Josh Vander Hook's user avatar
2 votes
Accepted

SLAM with multiple cameras and asynchronous sensors

I have done many multi-modal sensor fusions with LiDAR, IMU, and cameras. Obviously, synchronization is the hardest problem if it is not synchronized at the HW level. To be able to do SLAM with multi-...
Chanoh Park's user avatar
  • 1,577
2 votes

accelerometer and gyroscope fusion using extended kalman filter

You should be using quaternions for fusion for good behaviour. Addition and multiplication for quaternions will be swapped out by rotation composition operations for quaternions and your orientation ...
Raggy's user avatar
  • 128
1 vote

Sensor fusion with gyroscope and motor rotations

You need to describe your system as a linear system, preferably with a matrix notation. then change the base or make the state variables exactly what you want to measure, finally with an ¿observer' ...
Alfredo Maussa's user avatar
1 vote

Sensor fusion with gyroscope and motor rotations

If you’re using your gyros to measure the direction your bot is moving, you will need to use a PID system to adjust your direction based on the feedback your bot provides. This type of system ...
iamPres's user avatar
  • 41
1 vote

How do timings affect input, sensor fusion, and output?

How often should I read samples from a sensor (read the register over i2c) with relation to the frequency I configure the sensor to? If you sensor provide data at a certain rate you should not ...
N. Staub's user avatar
  • 1,412
1 vote

Calibrating a laser scanner to a line camera

I converted my comments to an actual answer: If I understand your setup correctly, you're saying you have a line scan camera mounted to the top of the rotating head of a laser scanner, and all you're ...
Chuck's user avatar
  • 16k
1 vote

How to model transition matrix in indirect kalman filter with external orientation estimate

After careful reading of the first paper I think the solution to the problem is to simply not include the multiplication. From the proof of 172b and equation 177, in my filter I have $R_t$ (since I ...
Nordmoen's user avatar
1 vote
Accepted

what kind of processing is required on raw IMU data before it is fed into a filter?

1) Before I go into filter design itself, I want to know do I just use the raw data from sensor ' as it is ' and feed it into filter system ? Or Do i need to some sort of preprocessing on data for ...
edwinem's user avatar
  • 1,871
1 vote
Accepted

How to Correct for Gyroscopic Drift?

I can propose you two solutions to solve your problem: The first one is a not easy to implement with a MEMS but it works: A Kalman filter (maybe more a Extended Kalman filter for navigation). The ...
Nick's user avatar
  • 26
1 vote
Accepted

Fusing IMU sensor with odometry

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.
edwinem's user avatar
  • 1,871
1 vote

How does one estimate motion from sparse encoders and IMU-Gyro data?

When you have one fast-moving source and want to fuse it with a slow-moving source, a complimentary filter should be sufficient. Hopefully, it's a lot easier to understand than Kalman filters. There ...
Dave Hillier's user avatar

Only top scored, non community-wiki answers of a minimum length are eligible