Hi,

Francois LE COAT writes:
>> Here's a drone's long flight through the forest in France...
>>

>>
>> We're in rather complicated lighting conditions, with shadows and
>> clouds. Hence the significant noise in the video. The optical-flow
>> (DIS - OpenCV) that allows the determination of monocular depth
>> performs rather well. Mathematicians refer to this determination
>> as an "ill-posed problem". But statistically, for the large images
>> we're dealing with, it works well :-)
> 
> Here's a sequence of images from a drone in the forest.
> 

> 
> These 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.
> 
> Here's the drone's trajectory in space: <https://skfb.ly/pCyBy>

Here's a sequence of images from a drone in the forest:

	<https://www.youtube.com/watch?v=ToRk-o1cD_4>

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 question that arises, then, is why do highly accurate inertial
navigation systems drift? Why are loop-closing methods, used with the
SLAM algorithm? Would an optical inertial navigation system be subject
to drift in the trajectory it estimates? That's debatable, isn't it?

Best regards,

-- 

<https://eureka.atari.org/>

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