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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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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Quadcopter sensor fusion

I'm interested in quadcopter position tracking using a mix of IMU and visual navigation. I want to implement an EKF filter but what's not clear to me is how the filter would be updated using the ...
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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 ...
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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 $\mu_u(k)$ ...
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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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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 ...
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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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Difference between observed errors and this error matrix in kalman filter?

I have been watching this video series to try to understand the kalman filter. The instructor has this layout explaining the process. What I don't understand is the difference in the error matrix <...
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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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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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How to calculate encoders odometry covariance

I am currently working on ROS based autonomous skid steered robot . as I have seen in the literature the base controller needs to subscribe to the geometry_msgs/cmd_vel topic output by the move_base ...
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How can i fusion gyroscope and accelerometer when accelerometer has only roll,pitch val?

I'm currently studying IMU Sensor with 6dof(gyro and accelerometer). My goal is to get Orientation of robot, and i found out that only 6dof imu sensor wouldn't be possible to get a correct Orientation ...
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EKF landmark-based help needed

I am new in localization algorithms and EKF is new for me as well. I think that I implemented EKF (the whole code of class is here. I have problems in updating bearing angle of the robot. Basically I ...
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Uncertainty in grid based fastslam

Given I am building a grid map, and poses are represented using particles, how can I calculate the uncertainty of a pose In a grid based fastslam 2.0?
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Advantage of Kalman filter in differential drive planar robot

We need to estimate the position and orientation of differential drive robot by using encoders and imu sensor. For this specific case, is there any advantage of using Kalman filter than taking the ...
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Transforming between a local and global frame for a Kalman filter

I want to track an object in a global Cartesian frame, denoted $G$. The object has a local frame, denoted $L$. The object is controlled by a velocity command $v$, defined in its local frame. This "...
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Where to include velocity in a Kalman filter

I am trying to understand some of the basics of the Kalman filter. The problem I am working on is quite complex, but I have spent some time trying to simplify the problem with an example. So, let's ...
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Some Kalman filter implementation queries

Just to clear some doubts: Qn 1. Does kalman filter require constant time step? From my own study, it does not seem necessary to have a constant time step. You just need to take into account time ...
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In a Kalman filter, what should be included in the state, and what should be included in the control?

Let's say I am controlling a robot using a velocity controller. This involves specifying a target velocity for the motors, which is achieved using a PID controller (the low-level details of this PID ...
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State-dependent Covariance in the Kalman Filter

When using a Kalman filter, one of the variables that must be defined is a matrix representing the covariance of the observation noise. In the implementations I have seen, this matrix is defined once, ...
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EKF linearization using Taylor expansion and absence of operating point

If we consider the first-order Taylor expansion of a general nonlinear function at the operating point $x=x_0$, then we have the following, $$f(x) \approx f(x_0) + \frac{\partial{f}}{\partial{x}}|_{x=...
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Robot localization in a known map without knowing its initial position in that map

Firstly I would like to say that I'm no expert in Bayesian Filters such as Kalman Filter and Particle Filter, but I've used the EKF before in a robot that has both wheel encoders and an IMU to ...
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Are Indirekt KFs with non-linear changing statematrix EKFs for the error state?

I'm trying to implement an indirect kalman filter to estimate the pose of a differential drive robot using gyroscope and wheel encoder data. I found a fiew papers (1 - 3) describing this approach but ...
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Implementing ESKF

I'm currently struggling with implementing the Multiplicative Kalman Filter or Error State Kalman Filter as described by Landis Markley in Attitude Error Representations for Kalman Filtering. Sadly ...
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Madgwick or Kalman filter for sensor fusion?

I was looking for some comparison between these two approaches, but couldn't find any. I am wondering, what are the actual differences in terms of power consumption, accuracy, convergence speed and ...
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Regarding kalman filter implementation

I think I might have originally posted this in the wrong stack exchange forum, link. I think this might be the right place, I am not sure if posting again is considered "bad style", I apologize if ...
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What are wheel ticks and wheel impulses?

I'm tracking the position of a vehicle along a certain trajectory using the Kalman filter and we have odometry data provided to us which gives the x-position of the vehicle, y-position of the vehicle ...
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Convert Vehicle coordinates to World coordinates for positioning

I'm tracking the position of a vehicle along a certain trajectory using the Kalman filter and the idea is to check for improvements in position estimation through fusion of data from multiple sensors (...
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How to synchronise data for fusion in Kalman from multiple sensors with different timestamp information?

I'm using Kalman filter to track the position of a vehicle and receive position data from 2 sensors: A GPS sensor and an Ultrasonic sensor for which I want to implement sensor fusion into the Kalman. ...
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Relative quaternion to global with uncertainty

in this perfect tutorial i found how i can compose two poses with uncertainty and how i can transform one representation to another with uncertainty http://ingmec.ual.es/~jlblanco/papers/...
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Differences between strapdown inertial navigation and motion models

In a state estimation scheme with a simple IMU/GNSS setup using EKF, I have always thought the prediction step would be done using a motion model and all sensor measurements would be incorporated via ...
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EKF SLAM : SLAM specific Jacobians for new landmarks

I am currently trying to understand the books SLAM for dummies and Simulataneous localization and mapping with the extended Kalman filter to implement slam. I have understood steps 1 and 2 SLAM for ...
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How do I set the weights of an Unscented Kalman Filter?

In most of the papers I have read, calculation of weights is done by the following formula: Where L is the dimensionality of my state and lambda is calculated by: And the usual values for alpha, ...
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Integreation Method In Dr with 3 Sources

I'm searching a navigation method to find my location while dead reckogning (gps signals jammed) with an imu and gsm based navigation results. In short i have 3 sources: Gsm Based Navigation ( gives ...
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Kalman Filter: Do we need to measure each entry of the output-sequence, or can we derive them?

A Kalman-Filter gets applied to a state-space model in order to obtain estimates of the state-vector. Example: Assume a car moving on a straight line in x-direction (this is a 1-dimensional problem)....
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Standard Kalman Filter applied to non-Gaussian noise data

A Kalman-Filter could be applied to sensor-readings in order to smooth them. The Kalman-Filter, however, assumes Gaussian distributed sensor-noise with zero-mean. Now, I found that my sensor-noise ...
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Sensorfusion of odometry, accelerometer and gyroscope using Indirect Kalman Filter

I'm trying to implement an indirect Kalman for pose estimation of a wheeled robot. I found two papers that describe this approach. CMU. Journal (2006) Vol. 5(1) IFAC Conference on Embedded Systems ...

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