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raulmur
ORB_SLAM2
C++2016–2020
raulmur / ORB_SLAM2

ORB_SLAM2

Real-Time SLAM for Monocular, Stereo and RGB-D Cameras, with Loop Detection and Relocalization Capabilities

6Distinct people
6Mentions
0Explicit recommenders
10.3KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Real-Time SLAM for Monocular, Stereo and RGB-D Cameras, with Loop Detection and Relocalization Capabilities

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 10:01:16 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year6 dated mentions

Observed mentions: 2016-10-06 15:22:44 UTC to 2020-08-19 07:48:54 UTC.

0122016-01-01 00:00:00 UTC to 2017-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments20162017-01-01 00:00:00 UTC to 2018-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2018-01-01 00:00:00 UTC to 2019-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments20182019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2020

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20162016-01-01 00:00:00 UTC2017-01-01 00:00:00 UTC202
20172017-01-01 00:00:00 UTC2018-01-01 00:00:00 UTC202
20182018-01-01 00:00:00 UTC2019-01-01 00:00:00 UTC000
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC101
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC101

What Hacker News says

1 sources
>what about at 2 seconds away @ 65 MPH? 65 MPH is about 30m/s, your average lidar should be good upto about 200 meters, which is about 6 seconds at 65 mph. > Will it detect a de-accelerating car faster than a camera that can detect a brake light? Now this is an interesting question. yes and no. A lidar will on its own will not give you object recognition. It will tell you that a reflective surface of size _n_ is directly in you proposed path, and that since last scan its got closer. From that you can make a very accurate obsticle map. I don't think that running a car soely on Lidar is actually all that feasible or a clever idea. Not without a boat load of mapping and processing to create a machine readable semantic map first. Having a camera array _as well as_ lidar is a very good idea, as it can provide blended information from the lida
Read the rest
r to the semantic map being generated by the camera and radar. Your example of brake lights is good, as it provides a cue as to what is likely to happen. It also means that the high latency of a visual processing system is less of a problem, as the model can be updated by the lidar. Camera picks up a car, the model attaches it to the pointcloud it thinks is the car from the lidar/radar. When the lidar/radar updates it can create a prediction of where the visual system should look. > detecting perpendicular cars vs parked cars at that range? You don't have to. lidar gives you a point cloud, those points can be roughly translated into hard surfaces. if a hard surface is in your path or predicted path, take action until it isn't. Dealing with pointclouds for object avoidance is much much more computationally simpler, than having to infer 3d from either structure from motion, stereo or both. https://github.com/raulmur/ORB_SLAM2 if you look at this slam system, the pointcloud it generates is very sparse compared to a lidar: https://youtu.be/W3ELziPYn5k?t=13 But as I said before, you need other sensors to get other data/corroborate world model. >lidar was quite sensitive to dust, rain, snow, and fog. It is indeed, like any other sensor > Will weather cause the lidar to decay in similar ways to human eyes? Most lidars operate in the far infrared. so will handle decay differently. Depending on frequency it'll either be sensitive to moisture, or not at all.
In Lidar Is a Crutch

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