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 ...
- 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 ...
- 15.9k
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 ...
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 ...
- 1,901
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 ...
- 802
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 ...
- 305
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 ...
- 338
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 ...
- 183
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 ...
- 2,814
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:
...
- 163
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 ...
- 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. ...
- 400
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. ...
- 2,452
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 ...
- 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 ...
- 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 ...
- 15.9k
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 ...
- 15.9k
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 ...
- 15.9k
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 ...
- 15.9k
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 ...
- 25.3k
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 ...
- 6,037
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....
- 336
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- ...
- 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 ...
- 4,445
2
votes
Addressing the sample impoverishment in particle filter
As said by Jacob, sample impoverishment is inherit to the Sampling-Importance-Resampling family of particle filters.
An alternative solution which does require some extra effort is to switch to a ...
- 21
2
votes
Accepted
Addressing the sample impoverishment in particle filter
Your description of sample impoverishment and the way to fix it seems about right. Resampling only when the variance gets low is doing exactly what you are asking for when you say the measurements ...
- 3,054
2
votes
Heading and Yaw Rate Measurements
You're looking for a heading sensor - do you mean a compass? Or is there something else you're looking for? A compass gives you an absolute measurement, and a gyroscope provides yaw rate information.
...
- 15.9k
2
votes
Localising a robot placed at an unknown position in a known environment
There are many tactics for localization in a known environment. the most popular ones are the Filtering methods which include Kalman filtering and Particle filtering. Nowadays the second one is the ...
- 400
2
votes
Determining position from a 2D map and LIDAR
I just wanted add on to both user12895 and AL-ROBOT's answers.
Based on experience:
What you need is an (Iterative Point Cloud) ICP algorithm.
Do not worry, if the robot cannot detect the entire ...
- 183
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