original BCRA Trans articles (Re: Loop Closures)

[WarrenZ]

The original articles published in BCRA Transactions are available as 
PDFs at 1a and 1b at the bottom of this list:
https://sites.google.com/site/patrickbwarren/publications

Patrick

On 06/11/2014 17:33, Wookey wrote:
> +++ Peter Smart [2014-11-04 17:12 -0500]:
>>   
>> Hi,
>>
>   
>> Can someone give me alink to somewhere that survey loop closures as
>> in Survex are carried out and documented?
> The very first issue of compass points had an article about the method here:
> http://www.chaos.org.uk/survex/cp/CP01/CPoint01.htm#Art4
>
>> Im OK with the general principle but don’t really understand the
>> stats and listing in Survex. I understand that the loop is broken
>> down to an out and back lines defined by nodes which link to other
>> points in the survey.BUT:
> Do you mean the output in the .err file?
>
>> 1) Is it moved a horizontal or sloping distance ie does it take
>> account of both plan and elevation loop closure? How much of
>> the error is then vertical versus horizontal?
> The least squares anaylsis in done in 3D. The err file splits out
> vertical and horizontal misclosures:
>
> so
> 161.twintubs.7 - 161.twintubs.3
> Original length   5.10m (  1 legs), moved   0.11m ( 0.11m/leg). Error   2.19%
> 1.237954
> H: 0.233058 V: 2.007189
>
> means that the segment (1 leg) between 161.twintubs.7 and
> 161.twintubs.3 has just over 2% error which is largely a vertical
> offset. I can't actually remember what units those numbers are in:
> Standard deviations, percent or metres. Neither of the last two seem
> right.
>
> Ol?
>
> This stuff could be much better docunmented in the manual. Anyone keen
> to do that?
>
>> 2) There are 3 numbers listed after the original length distance
>    moved etc line in the output. The first is on a line alone with no
>    designation the other two arelisted as H: nnn and V: nnn. Is this
>    information about the vertical and plan misclosureverall?
>
> Yes. The first number is the overall misclosure and the next line are
> the horizontal and vertical components of that. The units are a mystery although I'm sure this question has been answered before.
>
>> is the
>> loop closure routine progressive starting from small and working up
>> to large loops or is it an error minimisatiion optimisation type
>> scheme?
> It is a least squares analysis, one for each segmentable section of
> the data. The network can be chopped into sections at 'articulation
> points', which is points where there is only one edge (series of legs)
> between two more complex bits of network. Each of those can be solved
> eaparatly to reduce the memory usage and make things faster.
>
>> 4) Some of the legs involved in a loop end up with zero movement
>    despite the tie line moving substantially. This gives zero errorson
>    the out part of the loop and significant ones on the return
>    section. Is this simply a matter of geometry eg the error may be in
>    easting only so legs that have only a northing component aren’t
>    corrected?
>
> This could happen due to the geometry as you suggest. You would get
> zero adjustment in one direction if the error is entirely
> perpendicular to that, but for a survey segment of more than one leg
> that would never actually hapen as surveys always zig-zag a bit.
>
> More likely you have an extra constraint in the data such a *fix
> points with very low SDs attached so they constrain the segment
> between them.
>
>> Survex is capable of including different standard deviations for
>> individual survey lines or legs. Does the loop closure routine take
>> this into account eg by forcing more movement on a BCA grade 3 line
>> compared to BCRAGrade 5?
> Yes. Every leg/segment gets SDs assigned, either defaults due to the
> data type or BCRA grade info (just a set of SDs for readings). Errors
> are distributed according to the SDs. This is just a feature of the
> least squares algorithm
>
>> Issue is if you have a reliable survey how
>> do you force this to have more say in the final positions after loop
>> closure compared to the lower grade survey without simply fixing a
>> point at the end of the ‘good’ survey?
> You use the *SD command to allocate relatively low standard deviations
> to the data that is more accurate, and/or higher SDs to the data that is worse.
>
> Olly can proably give better answers on some of these points, but I
> hope that helped.
>
> Wookey


---
This email is free from viruses and malware because avast! Antivirus protection is active.
http://www.avast.com