4
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 based FastSlam, each particle updates its own grid-map using the data from the range sensor (Lidar, UltraSound, etc.) and its odometry. This is called "Mapping with ...
2
There is no secret. OctoMap was not really designed with pose refinement in mind. You can even see one of the authors of RGBD-SLAM mention it here.
If you did want to try and still use octomap with loop closure then the approach that you mention could work. You could probably make it a bit more efficient by only deleting the poses in Octomap that were ...
1
Actually, evaluating maps with different scales and densities happens to be part of my research for my PhD so here goes the two ways I can think of on the top of my head:
Visually: it is always the first step but not the only one ;).
Map matching methods as in this paper by Sören Schwertfeger. That sounds like the idea you had first and I think it would ...
1
I believe using a grid map will only decrease the accuracy of you estimates. I am assuming your feature map is basically a vector containing the position of each feature as real values. If you convert you feature to a grid map (occupancy grid), then you will be shifting every feature into a grid cell. This will result in a loss of resolution because the grid ...
1
The question is a bit old but an answer might help. I think you are getting confused by thinking of mapping, localization and exploration as separate processes in the context of grid-based FastSLAM. In the most basic form of the algorithm you have the three steps you described:
At every timestep :
Update the poses of your particles using your motion model
...
1
The referenced picture of an equation is this:
and in latex it is:
\begin{equation}\tag{26}
p(z_t|m,c_t) = \frac{1}{\sqrt{2\pi\sigma^2}} e^{-\frac{1}{2}\left\{
c_{t,*}\log{\frac{z^2_{max}}{2\pi\sigma^2}} +
\sum_{k=1}^{K_t}c_{t,k}\frac{(z_t-d_{t,k})^2}{\sigma^2} +
c_{t,0}\frac{(z_t-z_{max})^2}{\sigma^2}
\right\}}
\end{equation}
For a single ...
1
First off, occupancy grid mapping is just one possible representation of a map. I think your question really applies many map representations. Here are my thoughts.
Mapping without SLAM
Whether or not you can build a map from your sensor measurements without fully implementing SLAM depends on a couple things:
Do you have absolute sensors for localization? ...
1
Let's asume that our 2D occupancy grid map is called G.
Programmatically, G is a 2D array of float numbers between -1 and 1 called evidences.
Now, to update the map, we need to :
read the new laser mesurements
calculate the distances of obstacles from the robot
re-calculate the evidence values of G, and then
replace the old values by the new evidence ...
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