11–20 of 22 posts
Lidars are pretty powerful, but one big disadvantage of using point clouds for perception is that they are not colored. This makes identifying objects more difficult compared to camera images. However, by combining camera images with lidar data, we can enhance the point cloud by assigning colors to the points based on the corresponding camera image pixels. This makes visualizing and processing the point cloud much ea…
Lidars are pretty powerful, but one big disadvantage of using point clouds for perception is that they are not colored. This makes identifying objects more difficult compared to camera images. However, by combining camera images with lidar data, we can enhance the point cloud by assigning colors to the points based on the corresponding camera image pixels. This makes visualizing and processing the point cloud much ea…
It is pretty cool, we use it for detection of humidity degree or for species discrimination (e.g. plants, minerals, chemicals…).
Lidars are pretty powerful, but one big disadvantage of using point clouds for perception is that they are not colored. This makes identifying objects more difficult compared to camera images. However, by combining camera images with lidar data, we can enhance the point cloud by assigning colors to the points based on the corresponding camera image pixels. This makes visualizing and processing the point cloud much ea…
I'm not sure why you've just restated the first paragraph of the article.
Lidars are expensive, if you want spare point clouds, that are not quite real time you might want to check out colmap https://colmap.github.io/
Lidars are expensive, if you want spare point clouds, that are not quite real time you might want to check out colmap https://colmap.github.io/
Thanks, been trying to look into AI tools to generate point clouds from photos for a hobby robot. Crazy that a mediocre LIDAR costs more than every other part of the robot combined, maybe times 10.
https://github.com/leggedrobotics/open3d_slam
Its not AI, but it is simple and you can re-use a point cloud to re-localise against (ie once the map has been generated you can just localise rather than have to map the same time.)
Some places use ML to make a more robust descriptor (ie the thing that identifies the point in a point cloud) which mostly practical. I've not yet seen a practical "deep" SLAM pipeline. (but I'd not looked recently. )
I did work on this as part of my thesis quite a few years back at the university. One other optimization would be to process the points in parallel. Regarding the coloring of each 3d point, it might be feasible to not use one camera image, but a weighted sum of all camera images that can see the same point in the scene. Each pixel color is then weighted with the scalar product of the points normal and the viewing dir…
The way I handle the different camera images is to simply see which one provides a lower depth and use - with the idea that if the camera is closer, it would provide better information. But what you are suggesting is pretty interestint. I'm going to try that as well.
Lidars are pretty powerful, but one big disadvantage of using point clouds for perception is that they are not colored. This makes identifying objects more difficult compared to camera images. However, by combining camera images with lidar data, we can enhance the point cloud by assigning colors to the points based on the corresponding camera image pixels. This makes visualizing and processing the point cloud much ea…
> one big disadvantage of using point clouds for perception is that they are not colored That depends entirely on the capture device.
Would an accurate ELI5 of this be : * Mathematically align the photograph and the lidar point cloud. * For each photograph pixel, colour whichever aligned lidar point is closest to the camera. So you end up with one coloured lidar point per photograph pixel?