 |
|
|
|
|
|
| |
| |
|
|
|
|
| |
| |
|
|
Hi,
This is a large sequence of images from a drone in the forest:
<https://www.youtube.com/watch?v=22QoxEwMBQA> The forest
This image computing looks like SLAM (Simultaneous Localization and
Mapping) method. We obtain both the location (trajectory) and the
visible relief (3D depth map). However, we're dealing with a monocular
(single camera) image sequence, not a stereoscopic (human vision) one.
The sequence is constituted of 10 000 images at 60 frames per second.
It is both large and high-resolution.
Here's the drone's trajectory in space: <https://skfb.ly/pDCwR>
Best regards,
--
<https://eureka.atari.org/>
Post a reply to this message
|
|
| |
| |
|
|
|
|
| |
| |
|
|
Hi,
Francois LE COAT writes:
> This is a large sequence of images from a drone in the forest:
>
> <https://www.youtube.com/watch?v=22QoxEwMBQA> The forest
>
> This image computing looks like SLAM (Simultaneous Localization and
> Mapping) method. We obtain both the location (trajectory) and the
> visible relief (3D depth map). However, we're dealing with a monocular
> (single camera) image sequence, not a stereoscopic (human vision) one.
>
> The sequence is constituted of 10 000 images at 60 frames per second.
> It is both large and high-resolution.
>
> Here's the drone's trajectory in space: <https://skfb.ly/pDCwR>
This is a sequence of images from a drone in the forest:
<https://www.youtube.com/watch?v=Kt0mkFYX45A> Drone
The drone flies very slowly compared to the acquisition rate of 30
frames per second. This makes it much easier to observe the trajectory
and visible depth. The successive images in the sequence are highly
correlated. It is not necessary to frequently reset the reference image
selection over time, as the correlation between this image and
subsequent images remains well above the 60% threshold chosen for
resetting.
Best regards,
--
<https://eureka.atari.org/>
Post a reply to this message
|
|
| |
| |
|
|
|
|
| |
| |
|
|
Hi,
Francois LE COAT writes:
> This is a sequence of images from a drone in the forest:
>
> <https://www.youtube.com/watch?v=Kt0mkFYX45A> Drone
>
> The drone flies very slowly compared to the acquisition rate of 30
> frames per second. This makes it much easier to observe the trajectory
> and visible depth. The successive images in the sequence are highly
> correlated. It is not necessary to frequently reset the reference image
> selection over time, as the correlation between this image and
> subsequent images remains well above the 60% threshold chosen for
> resetting.
Here's a sequence of images from a drone in the forest:
<https://www.youtube.com/watch?v=hFcDUU626po> Swedish woods
This image computing called 3D Optical Inertia looks like SLAM
(Simultaneous Localization and Mapping) method. We obtain both
the location (trajectory) and the visible relief (3D depth map).
However, we're dealing with a monocular (single camera) image
sequence, not a stereoscopic (human vision) one.
Here is the spline trajectory in space: <https://skfb.ly/pFx6u>
Best regards,
--
<https://eureka.atari.org/>
Post a reply to this message
|
|
| |
| |
|
|
|
|
| |
