
Lidar mapping techniques using multiple sensors - derek_frome
https://www.ouster.io/blog-posts/2019/3/29/lidar-mapping-with-ouster-3d-sensors
======
Dangeranger
The SLAM approach will work well with a validated point cloud and a new set of
points for fixed objects. However if you are mapping movable or alterable
objects such as vegetation I am unsure if the algorithm will still yield
highly accurate results.

Another thing to consider is that if you are basing future measurements on
past measurements, you need to be accurate to less than 1cm in the absolute
X,Y,Z position of those points, and account for drift across your collection
area. Small errors will add up to large differences in the survey set.

~~~
ei8htyfi5e
I funded a paper mapping vegetation in a forest, if you're curious:
[https://www.philsalesses.com/s/a582379.pdf](https://www.philsalesses.com/s/a582379.pdf)

IIRC, the lidar still lined up mostly because tree stems tend to not move,
however, the larger problem was the error rate of the lidar sensor we were
using. Readings further than 10m and the Hokuyo we were using tended to
underestimate distances, so each scan of the forest looked a little but like
the floor was curving over like that scene from Inception. Although maybe only
20 degrees. Still enough to be annoying.

~~~
jefft255
Hey, I'm familiar with your work! I'm currently submitting similar work using
a Husky and a Velodyne HDL-32. I don't have the problem you mention with my
sensor. See: [https://www.youtube.com/watch?v=V-Q-
XWSWT-I&index=2&list=UUo...](https://www.youtube.com/watch?v=V-Q-
XWSWT-I&index=2&list=UUoAEeqlo7LxoJf6d88JOG6w)

~~~
ei8htyfi5e
Your video looks dope. What a difference 8 years makes.
[https://vimeo.com/16396416](https://vimeo.com/16396416)

------
colkassad
"Our SLAM algorithm is notable for being able to run in real time with not
just one, but three Ouster OS-1 devices at the same time, on a typical desktop
computer CPU."

What SLAM algorithm is that? Anyone know?

~~~
hellllllllooo
It's using ICP to register sucessive lidar scans. All three lidars are
calibrated so the relative positions are known and the data from all three can
be combined.

[https://en.m.wikipedia.org/wiki/Iterative_closest_point](https://en.m.wikipedia.org/wiki/Iterative_closest_point)

This alone isn't SLAM but can be used for odometry as part of a SLAM system.

~~~
jvanderbot
... which is not surprising they can register in near real time. ICP is not
that expensive.

------
vesche
LIDAR is really cool. The coolest use I know of is that a man named Steve
Elkins used it in Honduras to discover lost ancient archaeological sites a few
years ago. If you're interested read The Lost City of the Monkey God, it will
blow your hair back.

------
chris_mc
I wonder if you could position posts or boxes (some physical object) with
"weird" shapes that could be used as fixed, recognizable points for this sort
of thing? So when your sensor picks it up, it's easy to immediately know that
this specific object matches to object ID #1234 which is in a specific, known
lat/lon/altitude/rotation/translation position.

Something like steganography for these sensors in the real world:
[https://en.wikipedia.org/wiki/Machine_Identification_Code](https://en.wikipedia.org/wiki/Machine_Identification_Code)

~~~
pas
Well, we used this for local calibration:
[https://github.com/MarekKowalski/LiveScan3D/tree/master/docs...](https://github.com/MarekKowalski/LiveScan3D/tree/master/docs/calibration%20markers)
of course this is only to calibrate the feeds relative to each other.

But coupled with GPS almost any shape could work. (Hills, landmarks,
buildings.)

------
m3at
This is very interesting. Still too expensive for hobby projects, which is
fine as it's clearly not their target audience, but it made me wonder. A few
years ago, cheaper (albeit shorter range and less accurate) lidar were
predicted to be coming soon.

Searching in Chinese marketplaces didn't bring anything below ~$200, anyone
know about very low cost lidar?

~~~
msadowski
The cheapest one you will find will probably be Rplidar or ydlidar x4.

Those sensors will not be great, but you will be able to do SLAM with them.

Here is a review of X4 that I wrote earlier this year:
[https://msadowski.github.io/ydlidar-x4-review/](https://msadowski.github.io/ydlidar-x4-review/)

------
rsp1984
Great post! Some questions:

\- How strongly does the performance of the SLAM depend on the type of sensor
and the amount of sensors being used? I.e. I'm sure the performance using
three 128-channel sensors will be better than using one 16-channel sensor.

\- Will the software be made available to customers? If yes, as an SDK?

------
joshu
does anyone know anyone at ouster? i want to invite them to Self Racing
Cars[1] - would love to offer public datasets of a known location so people
can compare and contrast different platforms.

[1] [http://selfracingcars.com/](http://selfracingcars.com/)

------
brighton36
How can we invest in this?

~~~
dsl
They just closed a Series B for $60 million last month, bringing total raise
to $90 million. I think it is well outside the reach of individual investors
at this point.

~~~
brighton36
Is that a scandal?

