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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 ...
Gouda's user avatar
  • 902
6 votes
Accepted

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 ...
Chuck's user avatar
  • 16k
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 ...
Andreas Klintberg's user avatar
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, ...
Ian's user avatar
  • 11k
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 ...
edwinem's user avatar
  • 1,871
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 ...
Manuel Rodriguez's user avatar
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 ...
Guille Sanchez's user avatar
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 ...
Ricardo Achilles's user avatar
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 ...
Eric Lavigne's user avatar
4 votes

Process noise and Measurement noise in Kalman filter

Basically, the relative magnitude between process and measurement noise determines how much to trust a new sensor measurement. In one extreme, if the process noise is zero the kalman filter will ...
ryan0270's user avatar
  • 2,814
3 votes

Linearize a non linear system

The short answer to this question is that linearization won't work, and here's why: Differential drive robots can be modeled with unicycle dynamics of the form: $$\dot{z}=\left[\begin{matrix}\dot{x}\\...
JSycamore's user avatar
  • 926
3 votes
Accepted

Localising a robot placed at an unknown position in a known environment

The problem is that you can't apply path planning until you know where the robot is in the global coordinate frame. There are many localization techniques, and each has its pros/cons; I have used ...
JSycamore's user avatar
  • 926
3 votes

In EKF-SLAM, why do we even need odometry when there is a more reliable sensor?Also, are all SLAM algorithms feature-based?

Just to add up on this, using odometry to estimate the robot position is much faster than using data from a laser scanner. In most situations, data from a range scanner is handled as a 2D PointCloud. ...
bergercookie's user avatar
3 votes
Accepted

In vision based localization, is it possible to make multiple vehicles cooperate to improve the estimation of each other?

You can do it by fusion using a Kalman filter: You have a process model model: $$ x_t = g(x_{t-1},u_t) $$ Now, you have multiple measurements of the same process model from different perspectives: ...
Luis's user avatar
  • 163
3 votes

$p(m|x_t, u_t, x_{t-1})$ What does Thrun mean with the "map probability"?

How do I have to imagine $p(m|x_t,u_t,x_{t-1}$)? In his book, he kind of just handwaves it... In SLAM, you need to build two entities, the robot's state (i.e. position and direction) and the map. ...
CroCo's user avatar
  • 2,453
3 votes
Accepted

Kalman filter for vision based pose estimation: 'good' measurements not improving system covariance

First, let me see if I can summarize your issue to see if I understand correctly: 1) Start in area with bad measurements (high R), get P1 2) Move to area with good measurements (low R) 3) Move back to ...
ryan0270's user avatar
  • 2,814
3 votes
Accepted

Iterative Closest Point for 2-D LIDAR Data

You can use 3D feature descriptors here to register two point clouds. I've personally used two most recent ones that performed well enough for a similar application. Following are the references to ...
quartzfun's user avatar
  • 163
3 votes
Accepted

Mobile robot navigation without static map

I think you're confused on a few points. Mapping is when you try to build a map, given a known location. Localizing is when you try to locate yourself, given a known map. SLAM is simultaneous ...
Chuck's user avatar
  • 16k
3 votes

Why innovation equation in Extended Kalman filter is called innovation?

From the Wikipedia entry: In time series analysis (or forecasting) — as conducted in statistics, signal processing, and many other fields — the innovation is the difference between the observed ...
Chuck's user avatar
  • 16k
3 votes
Accepted

Calculate the Vehicle corner points given localization

Your vehicle heading is some angle, and the corner is some distance from the rear axle. The best coordinate system to use for angles and distances is polar. So, start with a vehicle layout like: I'm ...
Chuck's user avatar
  • 16k
3 votes

Extract laser scan from a map and known location

You can do it manually with a protractor and straight edge - put the center of your protractor on the scanner center, align your protractor's zero-angle mark to the scanner zero, then mark off ticks ...
Chuck's user avatar
  • 16k
3 votes

World and Map Frame for a real robot

As you've figured, static transforms are valid for fixed offsets such as sensor positions. They are the minimal solution the more complete recommended solution is to setup a robot model. There's ...
Tully's user avatar
  • 25.1k
3 votes

imu vs imu/data

The message type is the same - it's just a different topic name/namespace. For a robot that only has a single topic carrying IMU data, "imu" might be sufficient. However, many robots end up ...
Mike Ferguson's user avatar
3 votes
Accepted

Correct use of transformations while using coordinate frames

You're missing a couple key aspects: baselink -> odom transform is the location of the robot (where the robot thinks it is) relative to where it started as calculated by tracking the wheel encoders....
billy's user avatar
  • 336
2 votes

Autonomous Indoor Positioning System Robot based on CV approach

The camera should work fine as long as you can easily find the rover in the environment. An easy way to accomplish this is to place two different colored markers on the rover. By finding the markers ...
holmeski's user avatar
  • 1,853
2 votes

Is there an algorithm using the Kinect depth image (not the point cloud) for registration?

Consider this - what is the process of doing SLAM? First, get some sensor data, then move in the world, get some more sensor data, then try to do feature identification and matching to build up a map. ...
Chuck's user avatar
  • 16k
2 votes

All-in-one GNSS localization solution (hardware+software)

I have used both KVH CNS-5000 and the Xsens MTi-G-710 that do what you are asking. Both these products provide on-board sensor fusion. The CNS-5000 uses an EKF and I am not sure about the Xsens. They ...
Dominic Larkin's user avatar
2 votes

Understanding and correct drift when using BreezySLAM (aka tinySLAM / CoreSLAM)

Till now this is the easiest SLAM implementation that I've found. It works pretty well, however, there is a lot of room for improvement using the same principle used in the original code online. "1- ...
AL-ROBOT's user avatar
  • 319
2 votes

Understanding and correct drift when using BreezySLAM (aka tinySLAM / CoreSLAM)

You'll find that Gmapping works a lot better. I have used core slam quite a bit with the 04lx, tweaked the code, and tuned the algorithm. It works in a lot of cases, but... If you really want to ...
hauptmech's user avatar
  • 4,445

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