8 votes
Accepted

What is a good approach for outlier rejection during real time data filtering?

It is both acceptable and standard to use camera observations with a Kalman filter if you are talking about landmark positions in pixel or real-world space. Pixel space observations are usually ...
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  • 872
6 votes
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Why should I still use EKF instead of UKF?

Here are a few possible points of consideration. Certainly the UKF has many counterpoints where it has an advantage too. The most obvious advantage is computation power. Don't forget that ...
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  • 2,679
6 votes
Accepted

Whats the logic to implement a particle filter for a robot with range sensor?

Your question addresses three very different problems, all of which are hard with complicated research-type algorithms. Localization: When you have a known map of the environment and an unknown robot ...
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6 votes

Ambiguous definition of Error-State (Indirect) Kalman Filter

Hi and welcome to the wide, ambiguous, sometimes confusing world of research. But seriously, looking at 20 years of papers will sometimes produce these confusions. Let's look at what's going on. In ...
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6 votes
Accepted

What is the difference between Positioning and Localization Systems

In my opinion, the main difference is : Positioning : gives information about the robot coordinates. It gives raw data that you can use. Localization : it is the process of the robot (or other actor) ...
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  • 561
6 votes
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How to localise a underwater robot?

Localization under water was always a problem in ocean robotics as electromagnetic signals do not propagate very well in water. I think your best localization sensor in that case would be the good old ...
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  • 671
6 votes

Which is a good and cheap 3D LIDAR or other options?

There are now some sub and around ~1000USD 3D Lidars available. I wanted to provide an answer for future reference if anyone else comes looking for "cheap" Lidars. LeddarTech M16 ~500 USD ...
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5 votes

Accelerometer, gyro, and magnetometer sensor fusion in 2d

The gyrometer gives you angular velocity about each axis. You simply integrate these values to get the roll, pitch and yaw of the robot. Since this is 2D, all you care about is yaw, and you'll ...
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5 votes
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Build a ROS robot with SLAM without laser

A kinect mounted on your robot is enough for mapping and localization. There are a few different packages that will work: rgbdslam can create a 3d map using a kinect You can use ...
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  • 101
5 votes
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Robot localization without any sensors

If you know the wheel radius and the speed of the robot, you will be able to calculate its location at any time relatively to its initial position. ...
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5 votes

How can my robot find its position in any given map without GPS, including when the initial point is not given?

Determining your location when you have a map but not your starting location is a job for a particle filter. (Wikipedia's entry on particle filters is not very helpful to beginners.) See also, ...
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  • 10.8k
5 votes
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Kalman Filter GPS + IMU

This is a complete re-working of the answer I had originally provided. If you're curious, you can check the edit history and see what was posted earlier. In comments to this question, OP stated that ...
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  • 14.7k
5 votes

Difference between SLAM and Localization

Localization is always done with respect to a map. SLAM(Simultaneous Localization and Mapping). As it is in the name, also does localization with respect to a map. The only difference is that the ...
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  • 1,571
4 votes

Absolute positioning without GPS

I know this is an old question but I will just add a bit to the currently existing answers. First, this is a very complex problem that everyone is trying to tackle, including google with their Tango ...
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4 votes
Accepted

Multiple position estimates fusion

What you are describing is essentially a textbook case for using a Kalman filter. First you need a prediction step. Let's assume you are predicting the pose of the robot $(x,y,\theta)$, given the ...
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  • 950
4 votes
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Why models are not perfect to represent robotic environments?

A model of the environment in this context is an abstraction of the real world, which should be adequate for the task of the robot. For example, if you have a robot that needs to navigate an office ...
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  • 2,954
4 votes

Image based 3d position estimation with one camera

One camera gives you no depth information, so you have to have some information about the scene before you start (a priori). The most common way to handle this is with structured light, where you ...
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  • 14.7k
4 votes

Hector SLAM, Matching algorithm

Imagine someone put you in a wheelchair and blindfolded you, then let you reach your arm out and touch a wall. You could tell how far away the wall was, but as long as you were pushed parallel to the ...
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  • 14.7k
4 votes

How to localise a underwater robot?

If it's actually underwater, how about a webcam looking at the tile pattern on the floor? (Could be considered "cheating" as it will obviously fail in a natural lake, for example.) You can find a ...
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  • 1,021
4 votes

How to localise a underwater robot?

One of the prime sensors for global localisation on land is GPS. This is not an option underwater because electromagnetic waves get absorbed quickly. There are however alternatives, which provide ...
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  • 2,954
4 votes

How to have a 'Auto Go Home' feature, like the DJI Phantom 3, on a project built quadcopter?

I would go with one of two-ish methods to do this, but both methods require the craft to know its own position. You could do this with GPS, or an IMU, or any other means or combination of position ...
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  • 14.7k
4 votes

Understanding Drift in Simultaneous Localization and Mapping (SLAM)

Your intuition is mostly correct. Returning to where you started and re-observing landmarks you mapped earlier is called closing the loop in the SLAM literature. As you mentioned, your uncertainty ...
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  • 950
4 votes

Localization of a Robot to find it Coordinates according to the Known Map

Particle filter According to the OP a robot with at least a distance sensor is available and a map too. That's a nice starting point for developing a hypothesis tracker aka particle filter. At first ...
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4 votes

How does fast slam creates grid maps?

Grid based FastSlam relies on the same principle that Landmakr based FastSlam. The difference is that we are not working with each grid cell as a landmark, but the whole gridmap itself. For Grid ...
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4 votes
Accepted

Integrating GPS into Graph SLAM (how orientation fixed?)

You can use a very low information matrix value at the orientation elements of your state, given that the information matrix is the inverse of the covariance matrix. The covariance matrix ...
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4 votes
Accepted

Understanding and implementing belief space planning

The kalman filter that you've already been using on single robots can be broadened to apply to the swarm of robots. If you previously represented the state of a single robot with 5 variables, and you ...
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3 votes

Different Particle Filter min and max particle numbers give almost the same result

Once you have enough particles to resolve your position, the effect of adding more particles shrinks to zero. You are likely seeing the best possible results that your particle filter can achieve. ...
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  • 10.8k
3 votes

Accelerometer, gyro, and magnetometer sensor fusion in 2d

Using an IMU you can only measure: acceleration, rate of rotation, and direction of magnetic field. You cannot measure velocity, you can only integrate the acceleration to infer velocity. As you can ...
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  • 6,337
3 votes
Accepted

innovation step ekf localization?

Yes this is correct, given two assumptions: Each measurement is independent (i.e., the (Gaussian) distribution of observation $z_i$ is uncorrelated with $z_j$). Usually this is a fair assumption (e.g....
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  • 950

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