Questions tagged [kalman-filter]

A Kalman filter is an optimal estimator for linear dynamical systems with Gaussian noise. Extensions to non-linear systems are included through the Extended KF and Unscented KF.

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How to fuse IMU with encoders in EKF

Background I have a car-like mobile robot (4 wheels, where the forward ones are steering wheels) and I want to estimate its pose and velocity assuming 2D planar motion. I'm trying to solve this ...
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cartesian velocity control loop implementation

I'm using ROS (noetic) to intuitively control a franka manipulator using the panda_robot package for the simulation. I've set up an extended kalman filter which fuses the following measures: IMU data:...
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Would it make sense to use the unscented transform for linear problems too?

I've just learned about the unscented Kalman filter and I have a theoretical question. Suppose our innovation and measurement processes are linear but we know the initial state covariance and/or the ...
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Motion/System Model for range finder

I have a 1D Time-of-Flight based range finder that returns distance in mm. I am trying to implement a Kalman filter to get outlier-free estimation. The sensor measures the distance to the ground below ...
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Gating/Filtering spurious measurements for redundant sensors using Kalman Filter

I am using a simple Kalman filter to fuse two redundant range finders (1D) and the data is rich in outliers/spikes. The observation matrix is as follows: ...
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Localization by comparing current lidar scan with previous lidar scan

I have managed to use an ICP algorithm to produce a relative pose difference between a new lidar range scan and the previous lidar scan. When I tested it on individual scan pairs, the results look ...
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How to derive the kalman gain with the form of K=Pxz/Pz?

please see the formula and reference in the page https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/blob/master/10-Unscented-Kalman-Filter.ipynb Traditional formula of gain is expressed ...
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Kalman filter problem with the output

i want to use kalman filter to estimate my phone position, the measurments data is at this point just the accelerometer and the sampling rate is 3ms, i used the library pykalman, i have also wrote my ...
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robot_localization not fusing imu data

According to the documentation in : http://docs.ros.org/en/noetic/api/robot_localization/html/state_estimation_nodes.html I was able to transform the imu data header fram from "imu_link" ...
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Does the Bayes-Filter perform a convolution in the prediction step?

I am watching the (fantastic) SLAM lectures of Claus Brenner, where he introduces the Bayes-Filter (Kalman-Filter, Particle-Filter, Histogram-Filter). He says, that the prediction step involves the ...
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Mapping IMU readings from body frame to navigation frame

I'm trying to combine IMU displacements with the time of flight sensor readings in order to navigate through the indoor environment with a non-linear Kalman filter variant. In the graphic below, I ...
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Using EKF to fuse wheel encoders, IMU and GPS

I have a robot equipped with: 3D accelerometer measuring $a_x$, $a_y$, $a_z$ 3D gyroscope measuring $ω_x$, $ω_y$, $ω_z$ 3D magnetometer measuring $m_x$, $m_y$, $m_z$ wheel encoders measuring $v_l$, $...
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How to calculate the covariance and gain in SLAM when only one measurement is available?

I am trying to perform SLAM for cases where only one sensor measurement is available. For example, suppose I want to track the position of a robot moving in a room with multiple known landmarks (2D ...
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Can an Xbee transceiver module be used for controlling a stable Quadcopter?

I would like to use an Arduino Nano IOT as a flight controller and connect this to a Xbee transceiver module for control. If the Xbee module was set up so that for each packet transmission it included ...
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Sensor fusion with extended Kalman filter for roll and pitch

I'm trying to implement an extended Kalman filter to fuse accelerometer and gyroscope data to estimate roll ($\phi$) and pitch ($\theta$). I've found a lot of kalman filter questions but couldn't find ...
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GPS + IMU data and kinematics equations

I have the following data Longitudinal acceleration, $a_x^{IMU}$ Lateral acceleration, $a_y^{IMU}$ Vertical acceleration, $a_z^{IMU}$ Yaw angle, $\psi$ Yaw rate, $\dot{\psi}$ Latitude, $\rightarrow ...
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robot_pose_ekf won't publish any messages

I am having trouble getting the robot_pose_ekf package to publish messages. I launch it with this launch file. ...
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1 answer
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What would be a way to estimate IMU noise covariance matrix?

Weirdly enough, my robot platform which has an official ROS package supported by a manufacturer doesn't provide any covariance matrices of its sensors. So, I'm basically trying to estimate these ...
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UKF for a serie of observations with covariance

I have some doubts about how to implement a UKF-like algorithm when I only have motion observations and no control inputs. Assume I have a robot with state $s_t = (x_t, y_t, \theta_t)$ and the ...
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Nonlinear Sensor Fusion with Space-Time Finite Element and Static Condensation?

I have recently implemented an algorithm for the nonlinear fusion of GNSS, barometer, magnetometer, accelerometer and gyroscope data. The algorithm is based on a space-time finite element where the ...
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How to actually fuse sensor using Extended Kalman Filter

Background I'm working on 4-omniwheel mobile robot. It have encoder on each wheel and MPU 6050 IMU. The robot positioning suffer a great error because slip, so i try to increase the accuracy of ...
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Drone lost traj recovery

So I am using slam algorithm to localize the drone which is gps denied . The input to the slam algo is imu data and a video . Now after the first run of the slam algorithm it creates the trajectory ...
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1 answer
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No difference between UKF and EKF for SLAM

I built EKF and UKF SLAM algorithms. The problem is that I expected to see a difference because of the more precise approximation of the system in the UKF. Here's a screenshot from the estimated path ...
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Kalman filter for visual tracking of a ball sliding on a gutter

I'm working on a project where a robot needs to keep a ball at a desired position on a gutter. The gutter is fixed at one end and held at the other end by the robot’s ...
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Fusing cross-correlated measurements for mobile robot's localization using unscented kalman filter (ukf)

I'm currently working on a mobile robot's indoor localization. On the perception side, I can only rely on a 2D lidar and wheel odometry. I have used these sources as input of different localization's ...
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1 vote
2 answers
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GPS Course vs IMU Course

Im currently working with Kalman Filter for position and velocity, one of the important parameters that im using is the heading that the sensor fusion of the imu gives me, but i have seen that the GPS ...
2 votes
1 answer
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Kalman Filter with multiple inputs

Let's say I have one laser scanner and a radar device, which I should use to measure a distance to a wall (Fig. 1). Both devices are place on the same support, so... they should measure the same ...
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Particle Filter for IMU tilt angle and bias estimation from Kalman Filter models

I understand the functioning of Particle Filters from the book Probabilistic Robotics and the robotics course provided by Cyrill Stachniss. I want to implement, from scratch, a particle filter to ...
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IMU-Camera Senor Fusion

I am working on fusing IMU and Camera Sensor Fusion for the Drone to precisely land on the target location. With the Camera, I am tracking the April Tag which is on the ground. This gives me the x,y,z ...
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Kalman Filter Design

I'm new to Kalman filter design and I'm struggling to understand how to apply the Kalman filter methodology to my problem. I've read a research paper which seems to describe what I'm trying to do ...
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How do Particle Filters give estimates of uncertainty?

In the Kalman Filter the final covariance matrix is the estimate of the filter's uncertainty. How does one do so in Particle filters? Is it just the variance among the particles for each state? If so, ...
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Is Kalman filter really desired here?

I am trying to use Kalman Filter in my project to eliminate outliers that go beyond certain limit. I am use 1D lidar to get the distance between the robot and an object. I get pretty accurate values ...
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Is the covariance matrix in the extended Kalman filter guaranteed to be positive definite (ignoring numerical errors)?

I understand that due to numerical errors (e.g., round off error and machine precision) that the covariance matrix may not be positive definite, but if computers had infinite precision, is the ...
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Not getting expected performance from kalman filter+mahalanobis distance

I am using a 1D lidar in one of my projects and it returns the distance it measures, in millimeters (mm). At some point in time, it gives garbage values that go as high as 10,000 or higher, when the ...
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1 vote
2 answers
390 views

Sensor fusion of GNSS and IMU using UKF

I do have a land-based robot with an IMU and a GNSS receiver. From the IMU, I get the velocity and acceleration in both $x$ and $y$ directions. From the GNSS receiver, I get the latitude and ...
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IMU Vision Fusion using EKF

I am trying to track an object indoors using an IMU (only accel and gyroscope) and a visual marker. This is similar to IMU+GPS fusion, where GPS is effectively replaced by the position that my vision ...
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UKF for radar implementation

I'm struggling to implement Unscented Kalman Filter for tracking objects using radar. My state vector contains [x y z vx vy vz] and I can measure [rho phi theta velocity]. So everything looks trivial ...
1 vote
1 answer
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State propagation from uncertain control input

Consider a nonlinear system $x(k+1)=f(x(k),u(k))$, where $x(k)\in\mathbb{R}^{n}$ is the state, $u(k)\in\mathbb{R}^m$ is the control input. Here $u(k)$ is normally distributed RV with mean $\mu_u(k)$ ...
2 votes
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Kalman Filter for 2d pose

I'm really sorry if this is a dumb question, but I don't have a clue on how to do this. I'm trying to write a kalman filter with a State vector of : {x, y, ẋ, ẏ, ẍ, ÿ } To estimate the 2 dimensional ...
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2D Visual-Inertial Extended Kalman Filter

I am trying to implement an Extended Kalman filtering for combining IMU data and visual odometry in a simple 2D case where I have a robot that that can only accelerate in its local forward direction ...
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Kalman filter with missing dimension on measurement input

I am exploring the option of using a EKF with my differential drive robot. I do not have any prior experience with kalman filters. The robot that is under consideration has two wheel encoders for ...
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2 answers
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How do you handle angle discontinuities in estimation problems?

When one is implementing a state estimator in a system that involves kinematics, will inevitably face the problem of angle discontinuities, i.e., the fact that the angles have to be wrapped between ...
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Custom implementation of robot_localization package

I plan to implement a sensor fusion of IMU + Visual odometry using an EKF. I came across the excellent robot_localization package which does pretty much all that I want. However, I need to use perform ...
1 vote
1 answer
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Bias correction for multiple sensor fusion through Kalman Filtering

I am learning Kalman Filters and was working on a simple example: Temperature measurement of a room by using 4 thermometers(different biases and noises) if i consider that there is no bias in the ...
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Where do matrix A and A transpose come from in calculating the predicted covariance matrix?

I don't understand where the matrices A and A transpose come from in the equation in this series. I have done a one-dimensional ...
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EKF sensor fusion

What is the standard way to fuse multiple sensor measurements in an EKF framework? Say you have Odometry, IMU and some form of Lidar which can produce landmarks. EKF is normally presented as a ...
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EKF-SLAM what should the observation model be?

I am implementing an EKF algorithm for a drone localization, and while I was defining the observation model I got a bit confused. This is my situation: I have a drone which is able to give me the ...
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1 answer
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Kalman filter with a known motion model

I have a robot whose pose $(x, y)$ is defined relative to the global frame. I have a sensor which estimates the robot's current pose in the global frame, and the sensor is known to have Gaussian error....
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Can someone help me understanding reference frames in OxTS data in KITTI Vision Benchmark dataset?

If anyone has worked with KITTI dataset, can you explain the reference frame used in roll pitch yaw values? I downloaded the raw data from this link: http://www.cvlibs.net/datasets/kitti/raw_data.php ...
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what kind of processing is required on raw IMU data before it is fed into a filter?

Im working on quadcopter. At this stage im coding a reference system for quadcopter using 10DOF board.At this stage im at the point of only getting raw data values from accelerometer, gyroscope, & ...

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