[Geowanking] Probe based mapping of road network
Sylvan Ascent Inc.
sylvanascent at mail2web.net
Thu Nov 30 06:55:37 PST 2006
>>I would expect that GPS
>>readings would have error offsets in specific directions depending on
>>environment like a nearby building or terrain shape.
Has anyone actually made a study of this? I would tend to agree with this, but does anyone really know? Are there reliable ways to compensate? I've always thought that you could combine GPS feedback from lots of vehicles to produce an accurate real-time traffic map for better routing, with accurate street maps as envisioned here just as a nice bonus.
From: geowanking-bounces at lists.burri.to on behalf of stephen white
Sent: Mon 11/27/2006 8:43 PM
To: geowanking at lists.burri.to
Subject: Re: [Geowanking] Probe based mapping of road network
On 28/11/2006, at 10:50 AM, christopher.wilson at daimlerchrysler.com
> Goal: Create highly accurate and complete digital maps of the
> transportation network suitable for safety of life applications
> with accuracy commensurate with future GNSS systems (decimeters).
> It seems to me that this can only be done through a statistical,
> probe based, approach since imagery and 'mobile mapping' approaches
> are error prone with low revisit rates.
As with my previous posts to this mailing list, this is just going to
be my unsubstantiated opinion.
A statistical approach to GPS is assuming that the error margin is
perfectly uniform around the actual location. I would expect that GPS
readings would have error offsets in specific directions depending on
environment like a nearby building or terrain shape. Aerial imagery
is about the only thing that I would trust for this kind of accuracy
as it has the human factor of being able to eyeball for error.
Collaborative feedback (aka the community) would be the statistical,
probe based, approach to identify problems. You can still use error
margins to indicate the trustworthiness of such data, and gradually
add in extra information from merging several ways of collecting the
same data. I would investigate vision systems and image recognition
as an approach, as road markings are very easy for a computer to
identify. Lines and black bits.
Use GPS tracks to locate roads, then computer vision to extract road
information. GPS tracks contain additional information like turning
lanes, but trying to extract too much information from the same
source becomes a problem in filtering trends vs outliers. If you can
correlate against other sources of the same data, then you can make
more concrete deductions as well as being able to more easily verify
the data on the spot.
By aerial imagery, I don't mean Google Earth. I mean very high
resolution source data (used to make maps) so you can see all the way
down to the gum spots on the pavement. This is obviously not as easy
to collect as a bunch of GPS tracks, but you're going to find it a
very hard sell to attach life-saving importance to something that
politicians and the public can't see for themselves.
For that reason alone, you'll need to conclusively prove that your
tracks are accurate to that degree, which can only be verified by
plotting against the reality of the roads themselves.
Steve (the unknown guy without a famous website).
steve at adam.com.au
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