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andybarry
flight
C2017–2018
andybarry / flight

flight

Flight code for MIT CSAIL Robot Locomotion Group flying-through-forests project

4Distinct people
5Mentions
0Explicit recommenders
992GitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Flight code for MIT CSAIL Robot Locomotion Group flying-through-forests project

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 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 1:49:46 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 month5 dated mentions

Observed mentions: 2017-02-01 02:33:40 UTC to 2018-12-22 12:49:54 UTC.

012017-02-01 00:00:00 UTC to 2017-03-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 commentsFeb 20172017-03-01 00:00:00 UTC to 2017-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2017-04-01 00:00:00 UTC to 2017-05-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2017-05-01 00:00:00 UTC to 2017-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2017-06-01 00:00:00 UTC to 2017-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2017-07-01 00:00:00 UTC to 2017-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2017-08-01 00:00:00 UTC to 2017-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2017-09-01 00:00:00 UTC to 2017-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2017-10-01 00:00:00 UTC to 2017-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2017-11-01 00:00:00 UTC to 2017-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2017-12-01 00:00:00 UTC to 2018-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-01-01 00:00:00 UTC to 2018-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsJan 20182018-02-01 00:00:00 UTC to 2018-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-03-01 00:00:00 UTC to 2018-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-04-01 00:00:00 UTC to 2018-05-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2018-05-01 00:00:00 UTC to 2018-06-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2018-06-01 00:00:00 UTC to 2018-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-07-01 00:00:00 UTC to 2018-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-08-01 00:00:00 UTC to 2018-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-09-01 00:00:00 UTC to 2018-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-10-01 00:00:00 UTC to 2018-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-11-01 00:00:00 UTC to 2018-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-12-01 00:00:00 UTC to 2019-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsDec 2018

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
Feb 20172017-02-01 00:00:00 UTC2017-03-01 00:00:00 UTC110
Mar 20172017-03-01 00:00:00 UTC2017-04-01 00:00:00 UTC000
Apr 20172017-04-01 00:00:00 UTC2017-05-01 00:00:00 UTC101
May 20172017-05-01 00:00:00 UTC2017-06-01 00:00:00 UTC000
Jun 20172017-06-01 00:00:00 UTC2017-07-01 00:00:00 UTC000
Jul 20172017-07-01 00:00:00 UTC2017-08-01 00:00:00 UTC000
Aug 20172017-08-01 00:00:00 UTC2017-09-01 00:00:00 UTC000
Sep 20172017-09-01 00:00:00 UTC2017-10-01 00:00:00 UTC000
Oct 20172017-10-01 00:00:00 UTC2017-11-01 00:00:00 UTC000
Nov 20172017-11-01 00:00:00 UTC2017-12-01 00:00:00 UTC000
Dec 20172017-12-01 00:00:00 UTC2018-01-01 00:00:00 UTC000
Jan 20182018-01-01 00:00:00 UTC2018-02-01 00:00:00 UTC000
Feb 20182018-02-01 00:00:00 UTC2018-03-01 00:00:00 UTC000
Mar 20182018-03-01 00:00:00 UTC2018-04-01 00:00:00 UTC000
Apr 20182018-04-01 00:00:00 UTC2018-05-01 00:00:00 UTC101
May 20182018-05-01 00:00:00 UTC2018-06-01 00:00:00 UTC101
Jun 20182018-06-01 00:00:00 UTC2018-07-01 00:00:00 UTC000
Jul 20182018-07-01 00:00:00 UTC2018-08-01 00:00:00 UTC000
Aug 20182018-08-01 00:00:00 UTC2018-09-01 00:00:00 UTC000
Sep 20182018-09-01 00:00:00 UTC2018-10-01 00:00:00 UTC000
Oct 20182018-10-01 00:00:00 UTC2018-11-01 00:00:00 UTC000
Nov 20182018-11-01 00:00:00 UTC2018-12-01 00:00:00 UTC000
Dec 20182018-12-01 00:00:00 UTC2019-01-01 00:00:00 UTC101

What Hacker News says

5 sources
First off, having multiple sensors and integration of that data is never a bad thing in robotics; in fact, it is almost a necessary thing. More information can mean better decisions by the system driving the vehicle. More sensors can help fill in things other sensors can't "see". That said, you bring up a good point: Humans do a pretty good job driving vehicles with only a pair of eyeballs (usually), ears, and "feel" (I'm not sure how to put this last one, but a good driver tries to stay and feel "as one" with the vehicle, getting information about the road, various forces internal and external about the car, etc - in order to drive it properly and in control while making various maneuvers). So why couldn't we do the same with a self-driving vehicle? Well - the truth is - we can: https://devblogs.nvidia.com/parallelforall/deep-learning-se
Read the rest
l... https://images.nvidia.com/content/tegra/automotive/images/20... I mention that paper so often in these kinds of discussions, that I am sure people are getting sick of it. Note that in that paper, only a single camera is used - like driving with one eye closed (or blind in one eye), yet it still was able to do so properly. Part of Nvidia's purpose here is to advance self-driving vehicles, but they also are trying to sell self-driving vehicle manufacturers on their tech as well: http://www.nvidia.com/object/drive-px.html If the need for expensive and exotic LIDAR systems can be overcome, and simpler systems like cameras and radar can instead be used, it will both be a cheaper way to manufacture these cars, as well as being more reliable (as the article noted, it's one thing to make a LIDAR - making a reliable LIDAR for automotive use over 100s of thousands of miles/kilometers is a different ball-o-wax - but manufacturers already have experience with making radar and cameras for vehicles robust). For another take on using cameras for autonomous navigation - there's this project using high-speed winged drones (and completely open source - including the drone design files): http://spectrum.ieee.org/automaton/robotics/drones/mit-drone... https://github.com/andybarry/flight That one only uses a couple of small cameras for stereo vision. These two projects (and others if you care to search) both show that such a system is possible; we probably don't need LIDAR for the majority of driving scenarios. Radar can probably cover other areas, and you mention the concept of humans using hearing - but honestly I am not aware of any self-driving vehicle research on that potential kind of sensing, but if none exists, it seems like it might be a fascinating and promising area of study for someone! Audio sensors would also be another one of those "simple and robust" sensors for automobile usage that manufacturers would like. That said: If they can make a simple but robust LIDAR system, with no moving parts (such designs do exist, like flash LIDAR, for instance), and a low-cost (that part is key), having that on a vehicle certainly can't hurt. There are areas (particularly at night, or in inclement weather, or in other low-visibility situations) where a camera alone will have a similar trouble as a human would (though I wonder if a camera operating at other wavelengths (FLIR, Near-IR, and Near-UV for instance) could help?) In those situations, having a LIDAR might be the key to a safer driving experience for a self-driving vehicle, whereas a human without that ability might make a wrong (and potentially fatal) decision.
In Velodyne Announces a Solid-State Lidar

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