A Study on the Selection and Applicability Analysis of 3D Terrain Modeling Sensor for Intelligent Excavation Robot

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Received May 3, 2013/ revised May 21, 2013/ accepted May 24, 2013

Copyright ⵑ 2013 by the Korean Society of Civil Engineers

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0)

ǣǣȀȀǤǤȀͳͲǤͳʹ͸ͷʹȀǤʹͲͳ͵Ǥ͵͵Ǥ͸Ǥʹͷͷͳ ǤǤǤ

⽾ᡣ㯓#ጲ♫#Ḛ⋅ⴖ#ᇚ⇚ⴂ#Ⳃ㬚#Ḛ㒪⮿⮫#3㇦Ⲏ#⁦ᤶ὿#⛺⛚#⛞ⷓ#⇍#

㯂ⵣ#⶿Ⱨ⛯#⍂⛛⮎#ኾ㬚#⮮ጪ

କ෮জȵ֫০૵ȵ׌ઽজ

Yoo, Hyun-Seok*, Kwon, Soon-wook**, Kim, Young-Suk***

A Study on the Selection and Applicability Analysis of 3D Terrain Modeling Sensor for Intelligent Excavation Robot

ABSTRACT

Since 2006, an Intelligent Excavation Robot which automatically performs the earth-work without operator has been developed in Korea. The technologies for automatically recognizing the terrain of work environment and detecting the objects such as obstacles or dump trucks are essential for its work quality and safety. In several countries, terrestrial 3D laser scanner and stereo vision camera have been used to model the local area around workspace of the automated construction equipment. However, these attempts have some problems that require high cost to make the sensor system or long processing time to eliminate the noise from 3D model outcome. The objectives of this study are to analyze the advantages of the existing 3D modeling sensors and to examine the applicability for practical use by using Analytic Hierarchical Process(AHP). In this study, 3D modeling quality and accuracy of modeling sensors were tested at the real earth-work environment.

Key words : Intelligent excavation robot, 3D modeling sensor, Local area, Earth-work, Terrain

Ⅹಾ

2006֥ᇡ░ǎԕᨱᕽ۵ᬕᱥᯱ᮹}᯦ᨧᯕ᪥ᱥᯱ࠺⪵ႊ᜾ᮝಽ࠺᯲⦹۵ḡ܆⩶Ǖᔎಽᅨᮥ}ၽ⦹Łᯩ݅. ᯕ్⦽Ǖᔎಽᅨᮥ}ၽ⧉ᨱ

ᯩᨕᵝᄡ(ಽ⍍ᩢᩎ) ᯲ᨦ⪹Ğ᮹ḡ⩶ᯕӹᯕ࠺Ğಽᔢ᮹ᰆᧁྜྷ, Ǖᔎʑᨱᱲɝ⦹۵ᔢ₉✙౎॒᮹~ℕෝᱶ⪶⦹í┱ḡ⦹۵ʑᚁᮡ᯲ᨦ⣩ḩ ŝᦩᱥᖒ⪶ᅕ⊂໕ᨱᕽ⦥ᙹᱢᮝಽ᫵Ǎࡹ۵⧖ᝍʑᚁᯕ݅. ᖁḥ᫙ǎᨱᕽ۵☁Ŗᯱ࠺⪵ᰆእ᮹ಽ⍍ᩢᩎ3₉ᬱ༉ߙยᮥ᭥⦹ᩍŲݡᩎ3D ౩ᯕᱡᜅ⋱թӹᜅ▭౩᪅እᱥ⋕ີ௝᪡zᮡᖝᕽෝᔍᬊ⦹ᩍᵝᄡḡ⩶ᮥ3₉ᬱᮝಽ༉ߙย⦹۵ᩑǍෝᙹ⧪⦹ᩡᮝӹ, ᖝᝒ᜽ᜅ▽Ǎ⇶ᨱ

ḡӹ⊹í׳ᮡእᬊᯕᗭ᫵ࡹÑӹ༉ߙยđŝᨱיᯕᷩa݅ᙹၽᔾ⦹ᩍ༉ߙยᗮࠥaŝ݅⦹íᗭ᫵ࡹ۵ྙᱽᱱᯕᯩᨩ݅. ᯕᩑǍᨱᕽ۵ḡ

܆⩶Ǖᔎಽᅨ᮹ಽ⍍ᩢᩎ3₉ᬱ༉ߙยᮥ᭥⦹ᩍ⩥ᰍʭḡ}ၽࡽ3₉ᬱ᯲ᨦ⪹Ğ༉ߙยᖝᕽ᮹ʑᚁᔍ᧲ŝᰆ݉ᱱᮥᇥᕾ⦹Ł, AHP ᇥᕾ

ᮥ☖⦹ᩍᖝᕽᄥᱢᬊa܆ᖒᮥᇥᕾ⦽݅. ੱ⦽ᝅᱽ☁Ŗᔍ᯲ᨦ⩥ᰆ᮹⩥ᰆᝅ⨹ᮥ☖⧕⧕ݚᖝᕽ᮹3₉ᬱ༉ߙย⣩ḩŝᱶ⪶ᖒᮥᇥᕾ⦹Ł

ḡ܆⩶Ǖᔎಽᅨ᮹ಽ⍍ᩢᩎ3₉ᬱ༉ߙยᨱaᰆᱢ⧊⦽ᖝᕽᖁᱶၰ⩥ᰆᱢᬊᖒᮥá᷾⦹Łᯱ⦽݅.

áᔪᨕ ḡ܆⩶Ǖᔎಽᅨ, 3D ༉ߙยᖝᕽ, ಽ⍍ᩢᩎ, ☁Ŗᔍ, ᯲ᨦḡ⩶

֨ėěν

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1. ᕽು

1.1 ઴֜ଭࢼլࢫࡧୡ

2011֥ ÕᖅŖᔍ ᨦ᳦ᄥ ᙹᵝ⩥⫊(MOCT, 2011)ᨱ ঑෕໕

☁Ŗᔍ۵ᱥℕÕᖅŖᔍ23}ᨦ᳦aᬕߑ14.1%᮹እᵲᮥ₉ḡ⦹

ᩍ ℁ɝ ⎹Ⓧญ✙ Ŗᔍ(16.3%), ʑĥᖅእ Ŗᔍ(15.9%)ᨱ ᯕᨕ

ᖙჩṙಽ׳ᮡŖᔍእእᵲᮥ₉ḡ⦹۵ᵝ᫵Ŗᱶᯕ݅. ə్ӹ

☁Ŗᔍ ᯲ᨦᨱ ⚍᯦ࡹ۵ Õᖅ ᵲᰆእ ᬕᱥᬱ᮹ ᙺಉࠥӹ ⩥ᰆ

ᱢ᮲ࠥᨱ᮹⧕☁Ŗᔍ᮹᯲ᨦᔾᔑᖒᯕⓍí᳭ᬑࡹ۵॒ᩍᱥ⯩

י࠺Ḳ᧞ᱢ⥥ಽᖙᜅෝჸᨕӹḡ༜⦹Łᯩ۵ᝅᱶᯕ໑(Kim et al., 2011), ↽ɝᰆእᬕᱥᬱ᮹Łಚ⪵, ᙺಉŖᇡ᳒ᨱ঑ෙᔾᔑᖒ

ᱡ⦹, ᭥⨹᯲ᨦ⪹Ğᨱᕽ᮹ᦩᱥᖒ॒ᯕྙᱽ᜽ࡹ໕ᕽᯕᨱݡ⦽

ʑᚁᱢᯙݡᦩᮝಽ៉, ☁Ŗᔍšಉᰆእ᮹ᯱ࠺⪵ෝ᭥⦽ᩑǍ}ၽ

⦥᫵ᖒᯕ Ⓧí ᇡbࡹŁ ᯩ݅.

ǎԕ᮹Ğᬑ2006֥ᇡ░ǎ☁Ʊ☖ᇡ᮹ḡᬱᮥၼᦥ᪥ᱥᯱ࠺ᮝ ಽǕᔎ᯲ᨦᮥᙹ⧪⦹۵ḡ܆⩶Ǖᔎಽᅨ᮹}ၽᨱš⦽ᩑǍa

ᙹ⧪ࡹŁᯩ݅. ᪥ᱥᯱ࠺⪵ႊ᜾᮹ḡ܆⩶Ǖᔎಽᅨᮡᬕᱥᯱ᮹

}᯦ᨧᯕᯱᮉᵝ⧪, ḡ⩶3₉ᬱ༉ߙย, ᯱ࠺Ǖᔎ, ✙౎᮹ᯙ᜾

ၰᔢ₉᯲ᨦŝzᮡᯝಉ᮹᯲ᨦᮥࠦพᱢᯕŁḡ܆ᱢᮝಽᙹ⧪⦹

۵ᝁ}ֱ᮹Ǖᔎಽᅨᮥ᮹ၙ⦽݅. ᯕ్⦽᪥ᱥᯱ࠺⪵Ǖᔎಽᅨ᮹

}ၽᨱ ᯩᨕ Ǖᔎʑ ᵝᄡ ᯲ᨦ⪹Ğ᮹ ḡ⩶ᯕӹ, ᯕ࠺ Ğಽᔢ᮹

ᰆᧁྜྷ, Ǖᔎʑᨱᱲɝ⦹۵ᔢ₉✙౎॒᮹Ǖᔎಽᅨᵝᄡ~ℕෝ

ᱶ⪶⦹í ᯙ᜾⦹۵ ʑᚁᮡ ᯲ᨦ ⣩ḩŝ ᦩᱥᖒ ⪶ᅕ ⊂໕ᨱᕽ

⦥ᙹᱢᮝಽ᫵Ǎࡹ۵⧖ᝍ᫵ᗭʑᚁᯕ௝⧁ᙹᯩ݅. ၙǎ, ᯝᅙ

॒᮹ ᖁḥ ᫙ǎᨱᕽࠥ 3D ౩ᯕᱡ ᖝᕽӹ ᜅ▭౩᪅ እᱥ ॒᮹

᯲ᨦ⪹Ğ ᯙ᜾ ᖝᕽෝ ᔍᬊ⦹ᩍ ☁Ŗᔍ ᯲ᨦ⪹Ğᮥ 3₉ᬱᮝಽ

༉ߙย⦹ʑ ᭥⦽ ݅᧲⦽ ᩑǍෝ ᙹ⧪⧕ ᪵ᮝӹ, Ła᮹ ᜽ᜅ▽

Ǎ⇶እaᗭ᫵ࡹÑӹיᯕᷩa݅ᙹၽᔾ⦹ᩍ༉ߙย᜽eᯕŝ݅

⦹í ᗭ᫵ࡹ۵ ॒᮹ ྙᱽᱱᯕ ᯩᨩ݅.

ᅙᩑǍ᮹༊ᱢᮡ⩥ᰍʭḡ}ၽࡽ3₉ᬱ᯲ᨦ⪹Ğᖝᕽ᮹ʑᚁ

ᔍ᧲ŝᰆ݉ᱱᮥᇥᕾᮥ☖⧕ḡ܆⩶Ǖᔎಽᅨ᮹ಽ⍍ᩢᩎ3₉ᬱ

༉ߙยᨱᱢ⧊⦽ᖝᕽෝᖁᱶ⦹Ł, ⩥ᰆᝅ⨹ᮥ☖⧕ᝅᱽ☁Ŗᔍ

᯲ᨦ⪹Ğᮥ3₉ᬱᮝಽ༉ߙย⧉ᮝಽ៉ᖁᱶᖝᕽ᮹ᖒ܆ᮥá᷾⦹

۵äᯕ݅. ᅙᩑǍᨱᕽᖁᱶࡽ᯲ᨦ⪹Ğ༉ߙยᖝᕽ۵⇵⬥☁Ŗᔍ

᯲ᨦ⪹Ğ šಉ Õᖅ ᯱ࠺⪵ ᰆእෝ }ၽ⧉ᨱ ᯩᨕ ᱢᬊჵ᭥a

ๅᬑ մᮥ äᮝಽ ʑݡࡽ݅.

1.2 ઴֜ଭ࣐଍ࢫࢺ࣑

ᅙᩑǍ᮹ჵ᭥۵ḡ܆⩶Ǖᔎಽᅨᨱᕽᔍᬊࡹ۵Õᖅᯱ࠺⪵

ᰆእෝ}ၽ⧉ᨱᯩᨕᵝᄡḡ⩶ŝᰆᧁྜྷᮥᱶ⪶⦹í3₉ᬱᮝಽ

┱ḡ⦹ʑ᭥⦽ᖝᕽෝᖁᱶ⦹Ł, ⩥ᰆᝅ⨹ᮥ☖⧕ʑᚁᱢ┡ݚᖒ

ၰ⩥ᰆᱢᬊᖒᮥá᷾⦹۵äᯕ໑ᅙᩑǍ᮹ႊჶᮡ݅ᮭŝz݅.

1.2.1 ஺ۇ෴֠ॸߦࣱଭഠվॷୁડฅլ3ఙ଀ࡦ܄ࠫ׆২ଭ

୨ଭࢫண૬নंজ

ᅙᩑǍᨱᕽ۵ḡ܆⩶Ǖᔎಽᅨᮥ}ၽ⧉ᨱᯩᨕಽ⍍ᩢᩎ3₉ ᬱ༉ߙยʑᚁ᮹ᩎ⧁ŝ⦥᫵ᖒᮥᇥᕾ⦹Ł, ☁Ŗᔍ᯲ᨦ⪹Ğ᮹

3₉ᬱḡ⩶ߑᯕ░ෝᯕᬊ⦹ᩍḡ܆⩶Ǖᔎಽᅨᨱ᮲ᬊࢁᙹᯩ۵

ʑᚁᮥ ᇥᕾ⦽݅.

1.2.2 ஺ۇ෴֠ॸߦࣱଭ3ఙ଀஺෴ࡦ܄ࠫ׆২Թࢳ෮จࢫ

ࢂ୪୥ंজ

ᅙᩑǍᨱᕽ۵ᖁ⧪}ၽࡽ᪥ᱥᯱ࠺⪵ႊ᜾᮹Ǖᔎಽᅨ}ၽ⩥

⫊ ၰ 3₉ᬱ ḡ⩶ ༉ߙย ᖝᕽ ʑᚁᮥ ᇥᕾ⦹Ł, ᖁ⧪ }ၽࡽ

ʑᚁ᮹ྙᱽᱱᇥᕾᮥ☖⦹ᩍಽ⍍ᩢᩎ3₉ᬱ༉ߙยᖝᕽᖁᱶᮥ

᭥⦽ Łಅ᫵ᗭෝ ࠥ⇽⦹ᩡ݅.

1.2.3 ஺ۇ෴֠ॸߦࣱଭ3ఙ଀஺෴ࡦ܄ࠫଡ଍෉বਘ׆২

ंজࢫ1ఙবছট୨

ᅙᩑǍᨱᕽ۵⩥ᰍᔍᬊࡹ۵3₉ᬱ༉ߙยᖝᕽʑᚁ᮹✚Ḷ

ၰᔍ᧲ᮥ᳑ᔍ⦹ᩍᰆ݉ᱱᮥᇥᕾ⦹ᩡᮝ໑, AHP aᵲ⊹ᇥᕾᮥ

☖⦹ᩍ ☁Ŗᔍ ᯲ᨦ⪹Ğᨱ ᱢ⧊⦽ ᖝᕽෝ ࠥ⇽⦹ᩡ݅.

1.2.4 3ఙ଀஺෴ࡦ܄ࠫবছଭഠվॷୁડ෮ୋਓ෠ଡധ෉

ౖୡবছট୨ࢫ෮ୋୡ૳নंজ

ᅙ ᩑǍᨱᕽ۵ 1₉ᱢᮝಽ ࠥ⇽ࡽ 2᳦᮹ ᖝᕽෝ ᝅᱽ ☁Ŗᔍ

᯲ᨦ⩥ᰆᨱᱢᬊ⦹ᩍ༉ߙย▭ᜅ✙ෝᙹ⧪⦹ᩡŁ, ⧕ݚᖝᕽಽᇡ

░⫮ाࡽߑᯕ░ෝᇥᕾ⦹ᩍ3₉ᬱ༉ߙย⣩ḩၰᱶ⪶ᖒ⠪aෝ

ᙹ⧪⦹ᩍ↽᳦3₉ᬱ༉ߙยᖝᕽෝᖁᱶ⦹Ł, ᝅᱽ⩥ᰆᝅ⨹ᮥ

☖⧕ ⧕ݚ ᖝᕽ᮹ ⩥ᰆ ᱢᬊᖒᮥ á᷾⦹ᩡ݅.

2. ḡ܆⩶Ǖᔎಽᅨ᮹3₉ᬱ༉ߙยʑᚁ}ၽ⩥⫊ၰྙᱽ ᱱᇥᕾ

2.1 ஺ۇ෴֠ॸߦࣱଭഠվॷୁડฅլ3ఙ଀ࡦ܄ࠫ׆

২୨ଭࢫண૬ন

☁Ŗᔍ᯲ᨦ᮹ᔾᔑᖒŝ⣩ḩᮡ☁Ŗ᯲ᨦĥ⫮᮹⧊ญᖒᨱ᮹⧕

Ⓧí ᳭ᬑࡹ۵ ✚Ḷᯕ ᯩ݅. ☁Ŗᔍ ᯲ᨦ᮹ ⬉ŝᱢᯙ ᯲ᨦĥ⫮

ᙹพᮥ᭥⧕ᕽ۵ᰆእ᳦᳑ᯱ᮹Ğ⨹ᨱ᮹᳕⦹۵ʑ᳕᮹ႊ᜾ᨱᕽ

┩⦝⦹ᩍḡ⩶ᯕӹḡၹ✚ᖒᨱᱢ⧊⦽᯲ᨦĥ⫮ᙹพᯕ⦥᫵⦹݅.

☁Ŗᔍ᯲ᨦ⪹Ğᨱᕽ࠺᯲⦹۵Ǖᔎᯱ࠺⪵ᰆእ᮹⧊ญᱢᯙ᯲ᨦ ĥ⫮ᮥ᭥⧕ᕽ۵᯲ᨦݡᔢᯕࡹ۵ᝅᱽ☁Ŗᔍ᯲ᨦ⪹Ğŝ࠺ᯝ⦽

3₉ᬱᙹ⊹ᱶᅕʑၹ᮹aᔢ⪹Ğ(world model)᮹Ǎ⇶ᯕ⦥᫵⦹

໑, ☁Ŗᔍ᯲ᨦ᮹ḥ⧪ᨱ঑௝ᄡ⪵⦹۵ḡ⩶ᮥ3₉ᬱᮝಽᝅ᜽e

ᝁ(local model)⧁ᙹᯩᨕ᧝⦽݅. ੱ⦽ᯕ్⦽3₉ᬱaᔢ⪹Ğ

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Fig. 1. Global Area and Local Area Fig. 2. Autonomous Excavator of CMU (Stentz et al., 1999)

ᮥʑၹᮝಽᩢᩎᇥ⧁, ↽ᱢ⥭ఌ⡝᭥⊹ᖁᱶၰ᯲ᨦᙽ₉ᔾᖒᮥ

☖⧕↽ᱢ᮹☁Ŗ᯲ᨦĥ⫮ᮥᙹพ⧁ᙹᯩᨕ᧝⦽݅(Seo et al., 2007).

☁Ŗᔍ᯲ᨦ⪹Ğ᮹3₉ᬱaᔢ⪹ĞǍ⇶ᨱᯩᨕᝅᱽ☁Ŗᔍ

᯲ᨦ⪹Ğ᮹ ᱥၹᨱ Ù⊽ ⩥ᰆ݉᭥᮹ ḡ⩶ᮥ 3₉ᬱ ᙹ⊹ᱶᅕಽ

༉ߙย⦹۵äᮥɡಽჭ༉ߙย(global modeling)ᯕ௝ᱶ᮹⦹Ł, ᝅᱽ☁Ŗᯱ࠺⪵ᰆእa᯲ᨦᮥᙹ⧪⧉ᨱ঑௝ᰆእᵝᄡ᮹ᄡ⪵⦹

۵ḡ⩶ᮥᝅ᜽eᮝಽ3₉ᬱ༉ߙย⦹ᩍɡಽჭ༉ߙᮥᨦߑᯕ✙

(update)⦹۵äᮥಽ⍍༉ߙย(local modeling)ᮝಽᱶ᮹⦽݅.

ɡಽჭ༉ߙยᮡŲݡᩎ3₉ᬱ౩ᯕᱡᜅ⋱թෝᯕᬊ⦹ᩍ3₉ᬱ

ḡ⩶ ᱶᅕෝ ᔾᖒ⦹Ł, ᖅĥ ࠥ໕ŝ᮹ እƱෝ ☖⧕ ᯲ᨦᩢᩎ᮹

ჵ᭥᪡᯲ᨦᩢᩎ᮹᯲ᨦపᱶᅕෝᔾᖒ⦹۵ߑၹ⧕, ಽ⍍༉ߙยᮡ

☁Ŗᯱ࠺⪵ᰆእ᮹ᯕ࠺ᨱ঑௝ᄡ⪵⦹۵ᔢݡᱢᯙᩢᩎᮥ3₉ᬱᮝ ಽ༉ߙย⦹۵äᮝಽ៉, Fig. 1ŝzᯕ☁Ŗᯱ࠺⪵ᰆእa⦽

ḡᩎᨱ⥭ఌ⡝ᮥǍ⇶⦽⬥⫭ᱥ⇶ᮥᵲᝍᮝಽၹĞ15ၙ░ԕ᫙᮹

ᩢᩎᮥ ᮹ၙ⦽݅.

ಽ⍍ᩢᩎ3₉ᬱ༉ߙยʑᚁᮡ☁Ŗᯱ࠺⪵ᰆእ᮹ᵝᄡᨱ᳕ᰍ

⦹۵ḡ⩶ŝŁᱶੱ۵ᯕ࠺⦹۵ྜྷℕ᮹⩶ᔢᱶᅕෝ3₉ᬱᮝಽ

༉ߙย⦹۵ʑᚁᮥ᮹ၙ⦽݅. ੱ⦽~ℕᯙ᜾ʑᚁᮡḡ܆⩶Ǖᔎ

ಽᅨ᮹ᱥႊ᭥ᩢᩎ᮹3₉ᬱ༉ߙᮥᔢ᜽ᔾᖒ⦹ᩍḡ܆⩶Ǖᔎ

⦹۵ʑᚁᮥ᮹ၙ⦽݅. ಽ⍍ᩢᩎaᬕߑᱥႊ᯲ᨦᩢᩎ᮹ḡ⩶ᮡ

Ǖᔎ᯲ᨦᮥḥ⧪⧉ᨱ঑௝ḡᗮᱢᮝಽ⩶ᔢᯕᄡ⪵⦹ʑভྙᨱ

ᄡ⪵ࡽ ḡ⩶ᮥ ᯝᱶ ᵝʑಽ 3₉ᬱ ༉ߙย⦹ᩍ ə đŝෝ ᬱĊ

ᜅ▭ᯕᖹᮝಽᱥᘂ⧉ᮝಽ៉3₉ᬱaᔢ⪹Ğ᮹ᱥℕḡ⩶ᮥᝁ⦽

݅. ၹ໕, ḡ܆⩶Ǖᔎಽᅨ᮹ᱥႊ᭥ᩢᩎᖝᝒᮡḡ܆⩶Ǖᔎ

ಽᅨ ᵝᄡ᮹ ᔍ௭ᯕӹ ᔢ₉✙౎, ᰆᧁྜྷ ॒᮹ ᱶᅕෝ ᯙ᜾⦹ᩍ

ᯙ᜾ࡽ~ℕᱶᅕෝᬱĊᜅ▭ᯕᖹᮝಽᱥᘂ⦹Ł, ḡ܆⩶Ǖᔎಽᅨ ᯕǕᔎࡽ☁ᔍෝᔍ☁ੱ۵ᔢ₉⦹Ñӹᰆᧁྜྷ⫭⦝Ğಽෝᔾᖒ⧁

ᙹ ᯩࠥಾ ḡᬱ⦽݅.

⩥ᰍ☁Ŗᔍ۵ᰆእᬕᱥᯱaᮂᦩᨱ᮹⧕Ğ⨹ၰḢšᮝಽ

❱݉⦹ᩍ᜽Ŗ⦹Łᯩᮝအಽ, ٩ᮝಽᅕᯕ۵Ŗeᮥಽᅨᨱíᯙḡ

᜽┅ʑ᭥⧕⍕⥉░ԕᨱᕽᝅᱽŖeŝ࠺ᯝ⦽3₉ᬱŖeᮝಽ

⢽⩥⦹۵᯲ᨦᯕၹऽ᜽⦥᫵⦹݅. ḡ܆⩶Ǖᔎಽᅨ᮹᯲ᨦĥ⫮

(task planning) ᙹพᮥ᭥⧕ᕽ۵Ǖᔎಽᅨᯕᵝᄡḡ⩶ᮥᯙ᜾⦹

Ł, əᨱᱢ⧊⦽᯲ᨦᮥᙹ⧪⦹ࠥಾ⦹۵äᯕaᰆᵲ᫵⦹အಽ, እᱶ⩶ḡၹ ⩶ᔢ ᯙ᜾ʑᚁᮥ ☖⧊⦽ ಽ⍍ᩢᩎ༉ߙย ʑᚁᮡ

ྕᯙ᜽Ŗᮥ᭥⧕aᰆʑᅙᱢᮝಽw⇵ᨕᲙ᧝⦹۵ᬱ⃽ʑᚁᯕ௝

⧁ ᙹ ᯩ݅. ಽ⍍ᩢᩎ ᖝᝒᮥ ☖⧕ ⫮ाࡽ 3₉ᬱ ༉ߙᮡ ᬵऽ

༉ߙ ᝁ᮹ ༊ᱢ ᐱอ ᦥܩ௝ ḡ܆⩶ Ǖᔎ ಽᅨ᮹ Ǖᔎ Ğಽ

ᔾᖒ, Ǖᔎ᯲ᨦᱥ⬥᮹ḡ⩶እƱෝ☖⦽☁Ŗప᮹ᔑᱶ, 3₉ᬱ

CAD ᱶᅕ᪡᮹እƱෝ☖⦽Ǖᔎ⣩ḩáᔍ, ᔢ₉✙౎᮹ᱢᰍప

ᯙ᜾, ḡ܆⩶ Ǖᔎ ಽᅨ᮹ʕɪ ᱶḡ ၰᯕ࠺᜾ ᰆᧁྜྷ᮹ ⫭⦝,

☁Ŗ ᯱ࠺⪵ ᰆእ ⩲ᨦ ᜽ᜅ▽᮹ ᭥⊹ ᅕᱶ ၰ ∊࠭ ႊḡ ॒ŝ

zᯕᯱ࠺⪵ǕᔎᱥၹᨱÙℱ݅᧲⦽༊ᱢᮝಽ᮲ᬊࢁᙹᯩ݅.

2.2 ஺ۇ෴֠ॸߦࣱଭୁડ஺෴3ఙ଀ࡦ܄ࠫ׆২Թ ࢳ෮จंজ

ၙǎ᮹ ᪥ᱥ ᯱ࠺⪵ Ǖᔎ ಽᅨ ᩑǍ۵ 1999֥ ⋕օʑ ຽು

ݡ⦺Ʊ(CMU)᮹ᩑǍᨱᕽ᜽᯲ࡹᨩ݅. Stentz et al.(1999)ᮡFig.

2᪡zᯕǕᔎʑ᮹᫝἞ᔢ݉ŝ᪅ෙ἞ᔢ݉ᨱbbⅩݚ12,000}

᮹⡍ᯙ✙ෝᔹ⥭ย(sampling)⦹۵2ݡ᮹3D ౩ᯕᱡᜅ⋱թ(3D laser scanner)ෝᖅ⊹⦹ᩍᵝᄡ᯲ᨦḡ⩶ᮥ3₉ᬱᮝಽ༉ߙย⦹ᩡ

݅. ᯕ᜽ᜅ▽ᨱᕽ᪅ෙ἞ᜅ⋱թ۵ᱥႊ᮹ᄡ⪵⦹۵᯲ᨦḡ⩶ᮥ

3₉ᬱᮝಽ༉ߙย⦹ʑ᭥⦽ᜅ⋱թᯕ໑, ᫝἞ᜅ⋱թ۵Ǖᔎಽᅨ ᨱ ᱲɝ⦹۵ ᔢ₉✙౎ ੱ۵ ~ℕෝ qḡ⦹ʑ ᭥⦽ ᜅ⋱թᯕ݅.

3₉ᬱᮝಽ༉ߙยࡽḡ⩶ᮡ᯲ᨦ⥭௹թ(task planner)aᩢᩎᮥ

ᇥ⧁⦹ᩍ᯲ᨦĥ⫮ᮥᯱ࠺ᮝಽᙹพ⦽݅(Cannon, 1999). ᯕ᜽ᜅ

▽ᨱᕽbᜅ⋱թaᙹ⠪120ࠥ᮹ᩢᩎᮥqḡ⦹အಽ, Ǖᔎಽᅨᯕ

ᔢ₉✙౎᮹⩶ᔢᮥᱶ⪶⯩ᯙ᜾⦹Łᔢ₉᯲ᨦᮥᙹ⧪⦹ʑ᭥⧕ᕽ ۵Ǖᔎʑa☁ᔍᔢᇡᨱ᭥⊹⦹ᩍᔢ₉✙౎ᮥԕಅ݅ᅕ۵᭥⊹ᨱ ᕽ ᯲ᨦᮥ ᙹ⧪⧕᧝⦹໑, ᔢ₉ ✙౎ᮡ Ǖᔎʑ᮹ ᫝἞ ᭥⊹ᨱᕽ

ᱲɝ⧕᧝ ᔢ₉ᩢᩎ᮹ ᯙ᜾ᯕ ᬊᯕ⦽ ⦽ĥᖒᮥ ḡܩŁ ᯩ݅.

ᯝᅙ᮹ ᪥ᱥ ᯱ࠺⪵ Ǖᔎ ಽᅨ ᩑǍ۵ 2006֥ ᯝᅙ Õᖅᖒ

☁༊ᩑǍᗭ(Public Works Research Institute; ᯕ⦹PWRI)᮹

Yamamoto et al.(2006a)ᯕFig. 3ŝzᯕ2ݡ᮹2D ౩ᯕᱡᜅ⋱թ

᪡ 1ݡ᮹ ᜅ▭౩᪅ እᱥ ⋕ີ௝ ᜽ᜅ▽ᮥ ᰆ₊⦽ ᪥ᱥ ᯱ࠺⪵

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Fig. 3. Excavation Robot of PWRI (Yamamoto et al., 2006a)

Table 1. Factors Influencing 3D Modeling

Factors Supplementary details A. Economic Feasibility A1. Price of hardware

A2. Price of software

A3. Price of customizing and Installation of 3D modeling Sensor

B. Rapidity and Range B1. Quickness of data acquisition B2. Acquisition of data with 10 meters in

distance range

C. Accuracy C1. Accuracy of terrain model C2. Resolution of terrain model C3. Operation in night time

D. Installation D1. Installation difficulty on an Excavator D2. Sensor customizing difficulty E. Durability E1. Accurate data acquisition under

machine’s vibration E2. Waterproof

E3. Durability in harsh construction environment

Ǖᔎಽᅨᮥ}ၽ⦽äᯕ᜽Ⅹᯕ݅. Yamamoto et al.(2006b)᮹

ᩑǍᨱᕽ۵Õᖅ᯲ᨦ⪹Ğᯕӹ ⊂ᱶࢁ᪅ቭ᱾✙᮹᳦ඹᨱ঑௝

⊂ᱶᯕᮁญ⦽᜽ᜅ▽ᮥḡᗮᱢᮝಽ▭ᜅ✙⧕᪵ᮝ໑, ḡ⩶3₉ᬱ

༉ߙยᨱ۵ ᜅ▭౩᪅ እᱥ ᜽ᜅ▽ᮥ ᵝಽ ᔍᬊ⦽݅.

2.3 ஺ۇ෴֠ॸߦࣱଭౖୡবছট୨ଡ଍෉ճߙ૬ী

CMU᮹᪥ᱥᯱ࠺⪵Ǖᔎಽᅨᮡ2ݡ᮹3D ౩ᯕᱡᜅ⋱թෝ

Ǖᔎʑᔢ݉ᨱᖅ⊹⦹ᩍᱥႊ᯲ᨦᩢᩎ᮹Ǖᔎ᯲ᨦŝ᫝἞ᨱᕽ

ᱲɝ⦹۵ᔢ₉✙౎ᨱᔢ₉᯲ᨦᮥᙹ⧪⦹ࠥಾᖅĥࡹᨩ݅. ə్ӹ

ᯕᩑǍᨱᕽ۵Ǖᔎಽᅨᨱᰆ₊ࡽ3D ౩ᯕᱡᜅ⋱թ2ݡ᮹እᬊᮥ

ᨙɪ⦹Ł ᯩḡ۵ ᦫᮝӹ ᯝၹᱢᯙ Ųݡᩎ 3D ౩ᯕᱡ ᜅ⋱թ᮹

❱ๅaĊᮥqᦩ⧁ভݡݚ1ᨖᬱᯕᔢ᮹እᬊᯕᗭ᫵ࡹᨩᮥäᮝಽ

ᩩᔢࡹ໑, ᯕ᪡zᯕŁa᮹Ųݡᩎᜅ⋱թእᬊᮡᝅᬊ⪵ᨱⓑ

ᰆᧁaࡽ݅. ੱ⦽CMU᮹᪥ᱥᯱ࠺⪵Ǖᔎಽᅨᮡ᫝἞ᜅ⋱թa

ᔢ₉✙౎ᮥᯙ᜾⦹ࠥಾᖅĥࡹᨕᯩʑভྙᨱ✙౎ᯕᨙ޶᭥ᨱ

᭥⊹⦹Łᔢ₉✙౎ᯕ᫝἞ᨱᕽ ᱲɝ⦹ᩍᱶ⧕ḥ᭥⊹ᨱࠥ₊⧁

Ğᬑᨱอᱶᔢᱢᯙᔢ₉᯲ᨦᯕa܆⦹݅. ᷪ, Ǖᔎʑaᨙ޶ᦥ௹ᨱ

ᯩÑӹᔢ₉✙౎ᯕǕᔎʑ ⥭ఌ⡝अ⠙ᮝಽᱲɝ⦹۵Ğᬑᨱ۵

ᔢ₉ ᯲ᨦᯕ ᇩa܆⧉ᮥ ᮹ၙ⦽݅. ᝅᱽ Ǖᔎʑ۵ ᯲ᨦ ➉▕ᯕ

ๅᬑ݅᧲⦽ÕᖅʑĥᯕʑᨱǕᔎʑ⥭ఌ⡝ᯕᨕਅႊ⨆ᮝಽ᭥⊹

⦹޵௝ࠥ ᔢ₉᯲ᨦᯕ a܆⦹ࠥಾ ᖅĥࡹᨕ᧝ ⦽݅.

ᯝᅙPWRI᮹᪥ᱥᯱ࠺⪵Ǖᔎಽᅨᮡᦿᕽᨙɪ⦽ၵ᪡zᯕ

ᜅ▭౩᪅እᱥ⋕ີ௝᜽ᜅ▽ŝ2D ౩ᯕᱡᜅ⋱ܾ᜽ᜅ▽ᯕᰆ₊ࡹ

ᨕᯩŁ, ࢱ᜽ᜅ▽༉ࢱᱥႊ᮹᯲ᨦḡ⩶ᮥ3₉ᬱᮝಽᜅ⋱ܾ⦹ʑ

᭥⦽ᖝᕽᯕ݅. ᯕaᬕߑᱥႊḡ⩶᮹3₉ᬱ༉ߙยᮥ᭥⧕ᕽ۵

ᜅ▭౩᪅እᱥ᜽ᜅ▽(300อ⪵ᗭɪ)ᯕᔍᬊࡹ໑, ☁Ŗᔍ᯲ᨦḡ

⩶1⫭↍ᩢษ݅21Ⅹ᮹3D ༉ߙยᩑᔑ᜽eᯕᗭ᫵ࡹ۵äᮝಽ

ᇥᕾࡹᨩ݅(Yamamoto et al., 2009). ⦽⠙, PWRI᮹Ǖᔎಽᅨᮡ

ᱥႊ᮹ḡ⩶ᮥ3₉ᬱᮝಽ༉ߙย⧁ᙹᯩḡอ⊂໕ᯕӹ⬥໕ᨱᕽ

Ǖᔎʑᨱᱲɝ⦹۵~ℕ۵ᱥ⩡qḡ⧁ᙹᨧ݅۵ྙᱽaᯩ݅.

2D ౩ᯕᱡ ᖝᕽ᮹ Ğᬑ Ǖᔎʑ ᔢᇡa ᱶᗮᮝಽ ᖁ⫭⧁ Ğᬑ

ᵝᄡ360ࠥ᮹ḡ⩶ŝ~ℕߑᯕ░ෝ ᨜ᮥᙹᯩḡอ, Ǖᔎʑ᮹

ᦵ(arm)ࠥ⧉̹ᖁ⫭⧕᧝⦹ʑভྙᨱᵝᄡᱲɝ~ℕ᪡∊࠭ᯕ

ၽᔾ⧁ ᙹ ᯩ݅.

ʑ᳕᮹Ǖᔎᯱ࠺⪵ᩑǍ۵3₉ᬱḡ⩶ߑᯕ░ෝʑၹᮝಽǕᔎ

ၰᔢ₉᯲ᨦ᮹Ǎ⩥ᮥ᭥⦽݅᧲⦽ʑᚁ}ၽᯕᯕ൉ᨕᲙ᪵ᮝ໑, ḡ⩶ߑᯕ░᮹3₉ᬱ༉ߙยᯕӹ~ℕ᮹┱ḡෝ᭥⦽ᖝᕽᯙ░⟹ᯕ ᜅಽ۵ᵝಽᜅ▭౩᪅እᱥ⋕ີ௝᜽ᜅ▽ŝ2D ੱ۵3D ౩ᯕᱡ

ᜅ⋱թaᔍᬊࢉᮥ᦭ᙹᯩ݅. ᅙᩑǍᨱᕽ۵↽ᱢ᮹3₉ᬱ༉ߙย

ᖝᕽෝᖁᱶ⦹ʑ᭥⧕ǎ᫙ᨱᕽ}ၽࡽ᪥ᱥᯱ࠺⪵Ǖᔎಽᅨ᮹

ᖁ⧪ᩑǍྙ⨭ᮥ᳑ᔍ⦹Łྙᱽᱱᮥᇥᕾ⦽đŝ, ݅ᮭTable 1ŝ

zᯕ Ğᱽᖒ, ᝁᗮᖒ, ߑᯕ░ ⫮ाჵ᭥, ᱶ⪶ᖒ, ᖅ⊹a܆ ᩍᇡ, ԕǍᖒ ॒ᮥ Łಅ᫵ᗭಽ ࠥ⇽⦹ᩡ݅.

3. ḡ܆⩶Ǖᔎಽᅨ᮹ᵝᄡḡ⩶ 3₉ᬱ༉ߙยᮥ᭥⦽ᖝᝒ

ʑᚁᇥᕾ

3.1 3ఙ଀ࡦ܄ࠫবছ׆২ंজ 3.1.1 ֈ۩લ3D ߑଲୠਆ಑ەবছ

TOF(Time Of Flight) ႊ᜾᮹Ųݡᩎ3D ౩ᯕᱡᜅ⋱թ(terrestrial 3D laser scanner)۵݅ᯕ᪅ऽ(diode)ᨱᕽ౩ᯕᱡŲᮥၽᔾ⦹ᩍ

ྜྷℕ᮹⢽໕ᨱᕽᔑ௡ࡹŁ, ᔑ௡ࡽŲᖁ᮹ᯝᇡaญ᜽ქ(receiver)

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ಽࡹ࠭ᦥ᪅۵᜽eᮥ⊂ᱶ⧉ᮝಽ៉Ñญෝĥᔑ⦹۵ᰆእᯕ݅.

౩ᯕᱡŲ᮹b᳭⢽sᮡ⊂ᱱᬕ(point cloud)ᯕ௝⦹໑ᗭ⥥✙ᭉ ᨕෝᯕᬊ⦹ᩍbᱱ᮹᳭⢽sᮥᔝbๅ᜽(mesh)⪵⦹໕3₉ᬱ

᯦ℕᱶᅕෝ᨜ᮥᙹᯩ݅. Ųݡᩎ3D ౩ᯕᱡᜅ⋱թ᮹Ğᬑᯝၹᱢ ᮝಽ 300ၙ░ ᯕᔢ᮹ Ñญʭḡ ⊂ᱶᯕ a܆⦹ᩍ 3₉ᬱ ༉ߙย

ᖝᕽᵲaᰆຝÑญ᮹⊂ᱶᯕa܆⦹݅. ੱᜅ⋱ܾbࠥᖅᱶᨱ

঑௝ᙹၡญၙ░(mm) ⧕ᔢࠥಽ⊂ᱶ⧁ᙹᯩᮝအಽaᰆᱶၡ⦽

3₉ᬱ༉ߙยᖝᕽಽᅝᙹᯩ݅. ə్ӹᯝၹᱢᮝಽእᱥʑᚁ

ʑၹ᮹݅ෙ3₉ᬱ༉ߙยʑᚁᨱእ⧕ᜅ⋵ᗮࠥ(5~30ᇥ)aŝ݅

⦹í ᗭ᫵ࡹŁ, ᜽ᜅ▽ ᰆእ aĊ(1.2ᨖ~2.1ᨖᬱ)ᯕ ๅᬑ ׳ᮡ

݉ᱱᯕ ᯩ݅.

Ųݡᩎ3D ౩ᯕᱡᜅ⋱թ۵aᰆᝁ഑ᖒ׳ᮡ3D ༉ߙยđŝෝ

ᱽŖ⦹۵äᮝಽᅝᙹᯩᮝӹ, Ųݡᩎ3D ౩ᯕᱡᜅ⋱թෝḡ܆⩶

Ǖᔎಽᅨŝzᮡ☁Ŗᔍ᯲ᨦ⪹Ğᨱ࠺᯲⦹۵Õᖅᯱ࠺⪵᜽ᜅ▽

ᨱ ᱢᬊᮥ aᱶ⧁ Ğᬑ ໨ aḡ᮹ ʑᚁᱢ ྙᱽᱱᮥ ᩩᔢ⧁ ᙹ

ᯩ݅. ຝᱡŲݡᩎ3D ౩ᯕᱡᜅ⋱թ۵Łᗮᮝಽ࠺᯲⦹۵౩ᯕᱡ

Ñญ⊂ᱶʑෝ2}᮹ᱶᗮ⫭ᱥ⇶ᮥᯕᬊ⦹ᩍÑญෝ⊂ᱶ⦹۵

ᖝᕽಽ៉, ၹऽ᜽ᙹ⠪ᯕ฿ࠥಾᖅ⊹ࡹᨕ᧝⦹໑, ᜅ⋱ܾᮥ᜽᯲⦽

⬥ᔝbݡ॒ᨱŁᱶࡽᔢ┽ಽ᯲࠺ࡹᨕ᧝⦽݅. ✚⯩v⦽ḥ࠺ᯕ

ၽᔾ⧁Ğᬑ᪅࠺᯲ᯕၽᔾ⦹۵ኩࠥaๅᬑ׳ᮡྙᱽaᯩ݅.

ᯝၹᱢᯙ Õᖅᯱ࠺⪵ ᰆእ۵ ݡᇡᇥॵᲅ ᨵḥŝ ᮁᦶᰆእෝ

ᔍᬊ⦹ʑভྙᨱ⦥ᩑᱢᮝಽๅᬑv⦽∊Ċŝḥ࠺ᯕၽᔾ⦹໑,

᯲ᨦḥ⧪ᨱ঑௝࠺ℕ᮹ᯱᖙ۵ҫᯥᨧᯕᄡ⪵⦹íࡽ݅. ঑௝ᕽ

Ųݡᩎ3D ౩ᯕᱡᜅ⋱թ᮹ᱢᬊᮥ᭥⧕ᕽ۵ᯕෝŁಅ⦽܆࠺⩶

ḥ࠺qᙁᰆ⊹ၰ܆࠺⩶ᙹ⠪᳑ᱩᰆ⊹aၹऽ᜽ᖅ⊹ࡹᨕ᧝

⦽݅. ੱ, ☁Ŗᔍ᯲ᨦ⪹Ğᮡᯝၹᱢᮝಽᙹฯᮡᇥḥŝ᜖ʑᨱ

י⇽ࡽ⪹Ğᮝಽᅝᙹᯩᮝ໑, ☁Ŗᔍ᯲ᨦ⪹Ğᨱᔍᬊࡹ۵3₉ᬱ

༉ߙย ᖝᕽ۵ ၹऽ᜽ ႊḥႊ᜖ᨱ ݡ⦽ ᅕ⪙॒ɪ(international protection)ᮥ∊᳒⧕᧝⦽݅. Ųݡᩎ3D ౩ᯕᱡᜅ⋱թ۵ๅᬑ

ᱶၡ⦹Łၝq⦽ᰆእಽ៉, ᯝၹᱢᮝಽ۵ᔍ௭ᯕᰆእෝᯕ࠺⦹Ł

ᬕᬊ⦹۵äᮥᱥᱽಽᖅĥࡹᨩʑভྙᨱᔢݚ⯩ԏᮡ॒ɪ(IP-54) ᮹ᅕ⪙॒ɪᯕᱢᬊࡹᨕᯩ݅. ᯕ۵Ųݡᩎ3D ౩ᯕᱡᜅ⋱թෝ

☁Ŗᔍ᯲ᨦ⪹Ğᨱᱢᬊ⦹ᩍᬕᬊ⦹ʑᨱ۵ᱢ⧊⦹ḡᦫᮭᮥ᮹ၙ

⦽݅. ⦽⠙, Ųݡᩎ 3D ౩ᯕᱡ ᜅ⋱թෝ ᯲࠺᜽┅ʑ ᭥⧕ᕽ۵

ᜅ⋱ܾᗭ⥥✙ᭉᨕᬕᩢᯱaၹऽ᜽⦥᫵⦹ࠥಾᖅĥࡹᨕᯩᮝ໑,

᜽ᜅ▽⍅ᜅ░ษᯕḶ(customizing)ᨱၹऽ᜽⦥᫵⦽ᗭ⥥✙ᭉᨕ

௝ᯕቭ్ญ(library) ḡᬱࠥ ᔢݚ⯩ ᱽ⦽ᱢᯙ ྙᱽᱱᯕ ᯩ݅.

3.1.2 ਆഓߑૈणୢಉࡔޭ

ᜅ▭౩᪅እᱥ⋕ີ௝(stereo-vision camera)۵2ݡ᮹⋕ີ௝ෝ

☖⧕ ⫮ा⦽ 2₉ᬱ ᩢᔢᮝಽᇡ░ 3₉ᬱ ḡ⩶ ᱶᅕෝ ⇵⇽⦹۵

ʑᚁᯕ݅. ᯝᱶÑญෝࢱŁ႑⊹ࡽࢱݡ᮹⋕ີ௝ෝ☖⧕↍ᩢࡽ

ࢱ}᮹ᩢᔢᨱᕽ⦝ᔍℕeÑญ₉ෝǍ⦹Ł, ౭ᷩ᮹ⅩᱱÑญ᪡

⋕ີ௝ಽᇡ░ ⦝ᔍℕ ᔍᯕ Ñญ᮹ ʑ⦹⦺ᱢ Ǎ᳑ෝ ☖⧕ ᝅᱽ

ྜྷℕ᮹ 3₉ᬱ Ñญෝ ĥᔑ⦽݅.

ᜅ▭౩᪅እᱥ᜽ᜅ▽ᮡ2ݡ᮹CCD ⋕ີ௝อᮝಽǍᖒࡹʑ

ভྙᨱ౩ᯕᱡႊ᜾ᨱእ⧕aĊᯕᱡಕ⦹Łᱢ᫙ᖁၰⅩᮭ❭ႊ᜾

ᨱእ⧕׳ᮡᱶ⪶ࠥෝaḥ݅. ੱ⦽ᙽeᱢᯙᩢᔢ↍ᩢᮥʑၹᮝಽ

⦹ʑভྙᨱ ᜅ⋱ܾ ᜽eᯕ⦥᫵⦽ ౩ᯕᱡ ᜅ⋱ܾႊ᜾ᨱ እ⧕

3₉ᬱߑᯕ░⫮ाᗮࠥaๅᬑᬑᙹ⦽ᰆᱱᯕᯩᮝ໑, ᱥಆᗭ༉ప ᯕӹⓍʑ, ྕí॒ᨱᯩᨕᕽࠥᜅ▭౩᪅እᱥ⋕ີ௝۵౩ᯕᱡ

ႊ᜾ᨱእ⧕ᬵ॒⯩ᬑᙹ⦹Ł᜽ᜅ▽aĊࠥๅᬑĞᱽᱢᯙ⠙ᯕ݅.

ə్ӹᜅ▭౩᪅እᱥ᜽ᜅ▽ᮥᔍᬊ⦹ᩍ3₉ᬱ༉ߙยᮥḥ⧪⧁

ভ✚Ḷᱱᮥ ᯙ᜾⦹ʑ ᨕಅᬕᩢᔢᯝ Ğᬑ ᜅ▭౩᪅ᩢᔢ ๅ⋎

(matching)᜽יᯕᷩaၽᔾ⦹۵݉ᱱᯕᯩᮝ໑, ᜅ▭౩᪅እᱥ

⋕ີ௝᜽ᜅ▽ᮡᩢᔢᮥʑၹᮝಽ⦹ʑভྙᨱᯝ᳑⪹Ğᨱ঑௝

ᩢᔢ⣩ḩᨱⓑᩢ⨆ᮥၼŁ, ᩢᔢ⣩ḩᨱ঑௝ᱶ⪶ࠥ(accuracy)ෝ

⇵ᱶᮡ⧁ ᙹ ᯩḡอ౩ᯕᱡ ᜅ⋱թ⃹ౝ ᱶ⪶⯩ᅕᰆ⦹ḡ ᦫ۵

݉ᱱᯕ ᯩ݅. ᝅᱽ ᜅ▭౩᪅ እᱥ ⋕ີ௝ෝ ᯕᬊ⦹ᩍ ᜅ▭౩᪅

ๅ⋎ᯕᬊᯕ⦽ݡᔢྜྷ(ᯱᩑḡ⩶)ᮥ༉ߙย⦽ĞᬑእƱᱢᱶ⪶⦽

đŝෝᅕᩍᵝḡอ, ၹݡ᮹Ğᬑ✚⯩⥭ఌ⦽⠪໕ᮥaḥᯙŖ

Ǎ᳑ྜྷ(ᄞ, ĥ݉ ॒)ᮥ ༉ߙย⦽ Ğᬑ ᔢݚ⦽ ᪅₉᪡ יᯕᷩa

ၽᔾ⦽݅. ੱ݅ෙ݉ᱱᮝಽ۵3₉ᬱ༉ߙยჵ᭥a⋕ີ௝⪵bᨱ

᮹᳕⦹ʑভྙᨱ, 100ࠥᯕᔢմᮡ ჵ᭥᮹ḡ⩶ߑᯕ░ෝ᨜ʑ

᭥⧕ᕽ۵ᄥࠥ᮹ᖁ⫭༉░ෝᰆ₊⦹Ñӹ⫭ᱥbᨱ঑௝ᩍ్ᰆ᮹

ᩢᔢᮥ↍ᩢ⦽⬥⧊ᖒ⧕᧝⦹۵݉ᱱᯕᯩ݅. ᜅ▭౩᪅እᱥ⋕ີ௝

۵ እƱᱢ ḥ࠺ᨱࠥ v⦹Ł ᅕ⪙॒ɪࠥ እƱᱢ ׳ᮡ ⠙ᯕḡอ, ᝅ᫙⪹Ğᨱᕽי⇽ᔢ┽ಽᔍᬊ⧁ᙹ۵ᨧŁᅕ⪙⍡ᯕᜅෝᔍᬊ⦹

Ñӹ ᬕᱥᕾ ԕᇡᨱ ᰆ₊⦹ᩍ ᔍᬊ⧕᧝ ⦽݅.

3.1.3 ֜୺ֈ(structured light)

Ǎ᳑Ųʑᚁᮡʑ᳕᮹ᜅ▭౩᪅እᱥʑᚁᨱᕽ❭ᔾࡽäᮝಽ

ᇩᩑᗮᱢᯙ⢽໕ᨱݡ⦽ݡ᮲ᱱĥᔑᯕᬊᯕ⦹ḡᦫᮡᜅ▭౩᪅

እᱥʑᚁ᮹ᱶ⪶ࠥෝ⨆ᔢ᜽┅ʑ᭥⧕ᱽᦩࡽʑᚁᯕ݅. Ǎ᳑Ų

ႊ᜾እᱥʑᚁ᮹Ǎ⩥ᮡ⥥ಽ᱾░᪡zᯕᯝᱶ⦽Ƚ⊺᮹➉▕ᯕ

⡍⧉ࡽŲᬱᮥ3₉ᬱᮝಽᅖᬱ⦹Łᯱ⦹۵~ℕᨱ⚍ᩢ⦹Ł⋕ີ௝

ಽ ↍ᩢ⦽ ⬥ 3₉ᬱ ᯕၙḡ ᩢᔢᮥ ⫮ा⦹۵ ႊჶᯕ݅.

Ǎ᳑Ųႊ᜾᮹እᱥʑᚁᮡ዁ෙ᜽eᦩᨱๅᬑᱶၡ⦽3₉ᬱ

⊂ᱶᯕa܆⦹Łᖅ⊹a⠙ญ⦹໑ݡ᮲ᱱ᮹ ᱶ⪶ࠥෝ⨆ᔢ᜽┍

ᙹᯩ۵ᰆᱱᯕᯩ݅. ə్ӹ⥥ಽ᱾░ᨱᕽ⚍ᔍࡹ۵Ųᮥ⋕ີ௝a

ᯙ᜾⧁ᙹᯩᮥᱶࠥ᮹ၾᮡŲᬱᯕ⦥᫵⦽݉ᱱᮥḡܩŁᯩ݅.

↽ɝᨱ۵౩ᯕᱡŲᬱᮥݡᔢྜྷℕᨱ᳑ᔍ⦹۵ႊ᜾ᯕᵝಽᔍᬊࡹ

Łᯩᮝ໑, ᔢݚ⯩׳ᮡᱶၡࠥ᮹3₉ᬱ༉ߙยđŝෝ᨜ᮥᙹ

ᯩ݅. Ǎ᳑Ų᜽ᜅ▽ᮡɝᅙᱢᮝಽᝅԕ⪹Ğᨱᱢ⧊⦹ࠥಾᖅĥࡽ

(6)

Table 2. Performance Comparison of 3D Modeling Sensors

Description

3D Laser scanner (3DS.) 2D laser scanner (2DS.) Stereo Vision (S.V.) Laser based structured light (L.S.)

Data Aquisition speed 50,000 pts/sec 27,000 pts/sec 1,220,000 pts/shutter 17,920,000 pts/shutter

Sensing speed(70deg.) 32sec 2.4sec 0.1sec 2.4sec

Measurement

Range(deg) 360deg(H)×270deg(V) 270deg(V)

Data density(deg) 0.034deg 0.25deg0.5deg 0.05deg 0.045deg

Noise occurrence No noise No noise a little noise No Noise

Operation in night time No lights No lights Require extra lighting system Require extra lighting system Installable location Outside of Equip. Outside of Equip. Inside of Equip. Inside of Equip.

Laser class class 3R class 1 - class 3R

Power consumption

(W) 80W 8.4W 4W 7W

Weight (kg) 12kg 1.1kg 0.5kg 0.36kg

Operation Temperature

() 0~40 -30~50 0~45 0~45

Vibration

durability(Hz) - 10~150Hz - -

Shock durability(g) - 15g - -

Protection Level(IP) IP54 IP67 IP54 IP20

Price of Sensor() 150,000,000 5,500,000 4,700,000 12,200,000

Price of Installation and

Customizing 5,000,000 9,200,000 1,020,000 10,500,000

S/W Library supporting Partial restricted good normal normal

Customizing Difficulty normal easy easy difficult

Strength

- Long measurement range

- High accuracy - High 3D data density - No Noise

- No lighting system

- High accuracy, no noise, low system price - High protection level,

operating temperature suitable for field environment - High durability, easy

customizing

- Low power consumption, No lighting system

- High data acquisition speed - Color Information can be

acquired - Easy customizing - Low Power consumption - Low system price

- High Accuracy and High density

- Hight Speed modeling - Low power consumption - No noise

Weakness

- Long scan time - High system price - Low durability - High power consumption - Operating temperature is

unfit to field environment - Low protection level - Difficult to customize

sensor

- Rotating or moving equipment is needed to generate 3D terrain model

- Short measurement range - Low accuracy

- High noise

- Operating temperature is unfit to field environment - Low protection level - Require extra lighting

system

- Difficult to customize sensor - High system price

- Require extra lighting system - Low protection level - Operating temperature is unfit

to field environment

(7)

Table 3. Weights to Factors of 3D Sensor using AHP

Factors A B C D E Sum Wts.

A 1 2 1/2 3 4 1.22 0.24

B 1/2 1 1/4 3/2 2 0.61 0.12

C 2 4 1 6 8 2.44 0.49

D 1/3 2/3 1/6 1 2 0.45 0.09

E 1/4 1/2 1/8 1/2 1 0.28 0.06

Sum 4.03 7.75 2.01 12.50 22.00 5.00 1.00 A : Economic feasibility, B : Rapidity and Range, C : Accuracy, D : Installation, E : Durability

ʑᚁಽ៉ᝅᱽಽŲᬱŝ⋕ີ௝a⩲ᗭ⦽ᩢᩎᮥaʭᬕÑญᨱᕽ

ᜅ⋱ܾ⦹ᩍ Łၡࠥ᮹ ߑᯕ░ෝ ⫮ा⦹۵ߑ ↽ᱢ⪵ࡹᨕ ᯩ݅.

Ǎ᳑Ų᜽ᜅ▽ᮥ☁Ŗᔍ᯲ᨦ⪹Ğŝzᮡ᧝᫙⪹Ğᨱᱢᬊ⦹ʑ

᭥⧕ᕽ۵☖ᔢᱢᯙᯝ᳑⪹Ğᯕᔢ᮹ၾᮡŲᬱᯕ᫵Ǎࡹ໑, ᯕෝ

∊᳒⦹ʑ᭥⧕ᕽ۵౩ᯕᱡ3~4 ॒ɪ(class)ᯕӹ┽᧲Ųᅕ݅ၾࠥಾ

✚ᙹ⦹íᱽ᯲ࡽᯙŖ᳑໦ᯕ᫵Ǎࡽ݅. ə్ӹᝅᱽ॒ɪᯕ׳ᮡ

౩ᯕᱡŲᖁᮡ☁Ŗᔍ᯲ᨦ⪹Ğᨱᕽ᯲ᨦ⦹۵ᯙᇡӹ⊂పʑᔍ᮹

ᝁℕᨱ⊹໦ᱢᯙ⦝⧕ෝᵥᙹᯩᮥᐱอᦥܩ௝┽᧲ŲḢᔍŲᖁ᮹

ၾʑa↽ݡ130,000luxᨱᯕ෕۵ᱱᮥqᦩ⧁Ğᬑᔢݚ⯩׳ᮡ

ᱥಆᗭ༉ෝqݚ⧕᧝⦹۵ྙᱽᱱᯕᯩ݅. ੱ⦽10ၙ░ᯕᔢÑญ᮹

ḡ⩶ᨱŲᬱᮥ᳑ᔍ⧁Ğᬑኼᯕᔑ௡ࡹᨕŲᬱᮥᱶ⪶⦹íᯙ᜾⦹

ʑ⯹ुྙᱽᱱᯕၽᔾ⧁a܆ᖒࠥᯩ݅. ⦽⠙, ᔑᨦᇥ᧝ᨱᕽ۵

౩ᯕᱡŲᬱᰆ⊹᪡⋕ີ௝aŁᱶࡹŁ, ݡᔢྜྷℕa⍉ᄁᯕᨕᄉ✙

ෝ঑௝ᯕ࠺⦹۵ႊ᜾ᮝಽᵝಽᔍᬊࡹḡอ, ᧝᫙⦥ऽ⪹Ğᨱᕽ

Ǎ᳑Ų᜽ᜅ▽ᮥᔍᬊ⦹ʑ᭥⧕ᕽ۵Ųᬱᰆ⊹aᯕ࠺ੱ۵⫭ᱥᬕ

࠺ᮥ ⧁ ᙹ ᯩࠥಾ ᖅĥࡹᨕ᧝ ⦽݅.

3.1.4 2Dߑଲୠਆ಑ەবছ

2D౩ᯕᱡᜅ⋱ܾᖝᕽ۵Ųݡᩎ3D ౩ᯕᱡᜅ⋱թ᪡ษ₍aḡ ಽ౩ᯕᱡŲᮥݡᔢྜྷℕᨱᵝᔍ⦹ᩍəၹᔍŲᮥ⊂ᱶ⦹۵äᯕ

ʑᅙᬱญᯕ݅. Ųݡᩎ3D ౩ᯕᱡᜅ⋱թ۵Łᗮ᮹౩ᯕᱡÑญ

⊂ᱶʑ᪡2}᮹⫭ᱥ⇶(vertical, horizontal)ᨱᱶᗮᮝಽ⫭ᱥ⦹۵

Ñᬙ(mirror)ᮥᇡ₊⦹ᩍḡ⩶ᮥ3₉ᬱᮝಽ⊂ᱶ⦽݅. 2D ౩ᯕᱡ

ᜅ⋱թ۵Ųݡᩎ3D ౩ᯕᱡᜅ⋱թᨱእ⧕⦹ӹ᮹⇶ᯕᔾఖࡽ

⩶┽᮹౩ᯕᱡᜅ⋱թಽᅝᙹᯩᮝ໑, ᯝၹᱢᮝಽ20~50m ԕ᫙᮹

Ñญෝ ⊂ᱶ⧁ ᙹ ᯩ݅. 2D ౩ᯕᱡ ᜅ⋱թ۵ Ǎ᳑Ų ᜽ᜅ▽ŝ

ᮁᔍ⦹íḢᖁᮝಽᯕ࠺⦹۵⍉ᄁᯕᨕᄉ✙᜽ᜅ▽ᨱᕽᵝಽᔍᬊ

ࡹ໑ ᯕ࠺⦹۵ ྜྷℕ᮹ ⥥ಽ❭ᯝ(profile)ᮥ⫮ा⦹ᩍ 3₉ᬱᮝಽ

዁෕í ༉ߙย⦹ʑ ᬊᯕ⦹݅. 2D ౩ᯕᱡ ᖝᕽ۵ 270ࠥ ⠪໕ᮥ

↽ݡ 50Hz᮹ ᗮࠥ(Ⅹݚ 27,000 ⡍ᯙ✙)ಽ ᜅ⋱ܾ ⧁ ᙹ ᯩŁ, ᪅₉ࠥ±12 ၡญၙ░ᯕԕಽᱶ⪶ᖒᯕๅᬑᬑᙹ⦽⠙ᯕ݅. ੱ⦽

᯲࠺᪉ࠥa-30~50ⳃಽᝅ᫙⪹Ğᨱᕽᔍᬊ⦹ʑᨱๅᬑᱢ⧊⦹Ł, ḥ࠺ŝ ∊Ċᨱ ݡ⦽ ԕǍᖒᯕ v⦹݅. ᱥಆ ᗭ༉ప ⊂໕ᨱᕽࠥ

3D౩ᯕᱡᜅ⋱թᨱእ⧕10% ԕ᫙᮹ᱥಆᮥᗭ༉⦹۵ᰆᱱᯕ

ᯩ݅.

⦽⠙ 2D ౩ᯕᱡ ᖝᕽ۵ Ųݡᩎ ౩ᯕᱡ ᜅ⋱թᨱ እ⧕ ⊂ᱶ

ÑญaእƱᱢṈŁ, ᜅ⋱ܾbࠥ⧕ᔢࠥaእƱᱢԏʑভྙᨱ

3DŲݡᩎ౩ᯕᱡᜅ⋱թᨱእ⧕Łၡࠥ᮹3₉ᬱߑᯕ░ෝ⫮ा⦹

ʑ۵ᨕಖ݅۵݉ᱱᯕᯩ݅. bࠥ⧕ᔢࠥ۵⠪໕༉ߙยኩࠥ᪡

ᔢၹšĥ(trade-off relation)ᨱᯩʑভྙᨱbࠥ⧕ᔢࠥaԏᮝ໕

ၡࠥ ׳ᮡ 3₉ᬱ ߑᯕ░۵ ᨜ᮥ ᙹ ᨧḡอ, əอⓝ 3₉ᬱ ⠪໕

ߑᯕ░᮹ᝁᮡዉ௝ḥ݅. ᯕ్⦽2D ౩ᯕᱡᖝᕽ᮹✚Ḷᮡ☁Ŗ

ᔍ᯲ᨦ⪹Ğ᮹3₉ᬱ༉ߙยᨱ۵ᔢݚ⦽ᰆᱱᯕࢁᙹᯩ݅. ੱ

⦽aḡ݉ᱱᮝಽ2D ౩ᯕᱡᖝᕽ۵ᦿᕽᨙɪ⦽ၵ᪡zᯕ3D ౩ᯕᱡᜅ⋱թᨱእ⧕⦹ӹ᮹⫭ᱥ⇶ᮥᔾఖ⦽⩶┽᮹ᖝᕽᯕʑ

ভྙᨱ ☁Ŗᔍ ᯲ᨦ⪹Ğ᮹ 3₉ᬱ ༉ߙยᨱ ᔍᬊ⦹ʑ ᭥⧕ᕽ۵

ၹऽ᜽ᱶᗮᮝಽ᯲࠺⦹۵⫭ᱥʑǍӹḢᖁᯕ࠺ʑǍa⦥᫵⦹݅.

3.2 3ఙ଀ࡦ܄ࠫবছনۇॷઑण֗ࢫୋۚ୥ंজ ᅙ ᩑǍᨱᕽ۵ ᦿ ᱩᨱᕽ ᇥᕾࡽ Ųݡᩎ 3D ౩ᯕᱡ ᜅ⋱թ, ᜅ▭౩᪅እᱥ, ౩ᯕᱡŲᬱႊ᜾Ǎ᳑Ųᖝᕽ, 2D ౩ᯕᱡᖝᕽ

॒ᨱݡ⦹ᩍbᖝᕽᄥಽǎԕᨱᕽݡ⢽ᱢᮝಽᔍᬊࡹ۵༉ߙᮥ

ᖁᱶ⦹ᩡᮝ໑, ᯕ ᖝᕽॅᮥ ḡ܆⩶ Ǖᔎ ಽᅨᨱ ᰆ₊⦹ᩍ ᵝᄡ

ಽ⍍ᩢᩎᮥ3₉ᬱᮝಽ༉ߙย⧁Ğᬑෝaᱶ⦹ᩍ݅ᮭTable 2᪡

౩ᯕᱡŲᬱႊ᜾Ǎ᳑Ųᖝᕽ۵ᝅԕᬊᖝᕽ᮹ᖒ܆ᔍ᧲ᮥʑၹᮝ ಽᝅ᫙ᬊᮝಽᱽ᯲⦹ᩡᮥভ᮹⇵ᱶ⊹ෝᔑᱶ⦹ᩡ݅. ᅙᩑǍᨱᕽ ۵bᖝᕽ᮹ᖒ܆ᔍ᧲ᮥၵ┶ᮝಽ3₉ᬱ༉ߙยᖝᕽ4᳦᮹ᰆ݉ᱱ

ᮥᇥᕾ⦹ᩡᮝ໑, əđŝ2D ౩ᯕᱡᖝᕽaḡ܆⩶Ǖᔎಽᅨ᮹

ಽ⍍ᩢᩎ3₉ᬱ༉ߙยᨱaᰆᬑᙹ⦽ᖒ܆ᮥӹ┡ԝäᮝಽᩩᔢࡹ

Ł, ᜅ▭౩᪅እᱥ⋕ີ௝ᖝᕽaḡ܆⩶Ǖᔎಽᅨ᜽ᜅ▽ᨱᱢᬊ

a܆ᖒᯕ׳ᮡäᮝಽᇥᕾࡹᨩ݅. Ųݡᩎ3D ౩ᯕᱡᜅ⋱թ۵

ɡಽჭ༉ߙยᨱๅᬑᱢ⧊⧁äᮝಽᩩᔢࡹӹ᜽ᜅ▽aĊ⊂໕ᨱ ᕽಽ⍍ᩢᩎ༉ߙยᖝᕽಽᱢ⧊⦹ḡᦫᮡäᮝಽᇥᕾࡹᨩᮝ໑, ౩ᯕᱡŲᬱ ႊ᜾ Ǎ᳑Ų᜽ᜅ▽ᮡ ᝅ᫙ ⪹ĞᨱᕽᯙŖ Ųᬱ᮹

ᔾᖒᯕ ྙᱽa ࢁ äᮝಽ ᇥᕾࡹᨩ݅.

⦽⠙, ᅙ ᩑǍᨱᕽ۵ ḡ܆⩶ Ǖᔎ ᜽ᜅ▽᮹ ಽ⍍ᩢᩎ 3₉ᬱ

༉ߙยᖝᕽෝᖁᱶᮥ᭥⦹ᩍᦿᕽTable 1ᨱᕽ᳑ᔍᇥᕾࡽ5}᮹

Łಅ᫵ᗭෝݡᔢᮝಽAHP(Analytic Hierarchy Process) ᇥᕾʑ ჶᮥᯕᬊ⦹ᩍŁಅ᫵ᗭᄥaᵲ⊹ෝᇥᕾ⦹ᩡᮝ໑, 3.1ᱩᨱᕽᇥᕾ

ࡽ4}᮹ᖝᕽᨱݡ⦹ᩍᖙᇡŁಅ᫵ᗭ᮹⧕ݚᩍᇡá☁ෝ☖⦹ᩍ

ᖝᕽᄥᖁ⪙ḡᙹෝᔑᱶ⦹ᩡ݅. ᦥ௹Table 3ᮡ10໦᮹እᱥᖝᕽ

ᱥྙaၰᄅ޵(vendor)ॅŝ᮹ቭ౩ᯙᜅ☁ၮ☖⧕AHP ᇥᕾႊჶ

ᮥʑၹᮝಽŁಅ᫵ᗭe᮹ᝮݡእƱෝ⦽đŝಽ៉, ᱶ⪶ᖒ(C)᮹

(8)

Table 4. Analysis of Supplementary Details

Factors Supplementary details 3DS. 2DS. S.V. L.S. Sum A(0.24) A1. Price of hardware    

A2. Price of software     A3. Price of customizing and

Installation    

Score 1 4 6 2 0

Ratio 0.08 0.31 0.46 0.15 1.00 B(0.12) B1. Quickness of data acq.    

B2. Acquisition of data with 10

meters in distance range    

Score 2 3 3 2 0

Ratio 0.20 0.30 0.30 0.20 1.00 C(0.49) C1. Accuracy of terrain model    

C2. Resolution of terrain model     C3. Operation in night time    

Score 4 4 3 3 0

Ratio 0.29 0.29 0.21 0.21 1.00 D(0.09) D1. Installation difficulty on an

Excavator    

D2. Sensor customizing

difficulty    

Score 1 3 3 1 0

Ratio 0.13 0.38 0.38 0.13 1.00 E(0.06) E1. Accurate data acquisition

under machine’s vibration    

E2. Waterproof    

E3. Durability in harsh

construction environment    

Score 1 6 2 1 0

Ratio 0.1 0.6 0.2 0.1 1.00

A : Economic feasibility, B : Rapidity and Range, C : Accuracy, D : Installation, E : Durability

Ggood(2) Gnormal(1) Gbad(0)

Table 5. Preference Index of 3D Modeling Sensors

Factors A B C D E P.I.

3DS. 0.018 0.024 0.140 0.012 0.006 0.200 2DS. 0.074 0.036 0.140 0.034 0.036 0.320 S.V. 0.111 0.036 0.105 0.034 0.012 0.298 L.S. 0.037 0.024 0.105 0.011 0.006 0.183 A : Economic feasibility, B : Rapidity and Range, C : Accuracy, aᵲ⊹a0.49ಽaᰆ׳íӹ┡ԍᮝ໑, Ğᱽᖒ(A)ᯕ0.24, ᝁᗮᖒ

ၰ⊂ᱶჵ᭥(B)a0.12, ᖅ⊹ᬊᯕᖒ(D)ᯕ0.09, ԕǍᖒ(E) 0.06

ᙽᮝಽ ᇥᕾࡹᨩ݅.

ᅙᩑǍᨱᕽ۵ᦿᕽ᳑ᔍࡽ4}᮹ݡᦩ(Ųݡᩎ3D ౩ᯕᱡᜅ⋱

թ(3DS), 2D ౩ᯕᱡᜅ⋱թ(2DS), ᜅ▭౩᪅እᱥ(S.V), ౩ᯕᱡ

ႊ᜾Ǎ᳑Ų(L.S)) ᵲᝅ᜽eḡၹ3₉ᬱ༉ߙย᜽ᜅ▽Ǎ⩥ᮥ

᭥⦽↽ᱢݡᦩᮥ₟ʑ᭥⧕Table 4᪡zᯕbŁಅ᫵ᗭᄥᖙᇡ

Łಅ᫵ᗭᨱݡ⦹ᩍbݡᦩ᮹⧕ݚᩍᇡෝá☁⦹ᩡ݅. Table 5᮹

bݡᦩᨱݡ⦽ᖁ⪙ḡᙹ(preference index)۵Table 3ᨱᕽࠥ⇽⦽

Łಅ᫵ᗭᄥaᵲ⊹᪡Table 4ᨱᕽࠥ⇽⦽᫵ᗭʑᚁᄥᖙᇡŁಅ᫵

ᗭ᮹⧕ݚእᮉᮥŒ⧉ᮝಽ៉᨜ᮥᙹᯩ݅. ᷪ, bݡᦩ᮹ᖁ⪙ḡᙹ

ෝᔑᱶ⦹ʑ᭥⦽ᔑᚁ᜾ᮡ݅ᮭEq. (1)ŝzᯕ5}᮹ᵝ᫵Łಅ᫵

ᗭᨱݡ⦽aᵲ⊹᪡bᵝ᫵Łಅ᫵ᗭᄥᖙᇡŁಅ᫵ᗭ᮹⧕ݚእᮉ

ᮥ Œ⦽ ⧊ᮝಽ ᱶ᮹ࢁ ᙹ ᯩ݅.

⁷㖏⤗ⅯáāÞష᝻◫⃣ ୗ⡨⶯Z ₏᳗◫⃣ 㓋ቐᶛ♿ß (1)

ᩩಽ៉,

3D ౩ᯕᱡ ᜅ⋱թ᮹ ᖁ⪙ḡᙹ =

Ğᱽᖒ᮹ aᵲ⊹ 0.24 × ᖙᇡ᫵ᗭ ⧕ݚእᮉ 0.08 + ᝁᗮᖒ᮹ aᵲ⊹ 0.12 × ᖙᇡ᫵ᗭ ⧕ݚእᮉ 0.20 + ᱶ⪶ᖒ᮹ aᵲ⊹ 0.49 × ᖙᇡ᫵ᗭ ⧕ݚእᮉ 0.29 + ᖅ⊹ᖒ᮹ aᵲ⊹ 0.09 × ᖙᇡ᫵ᗭ ⧕ݚእᮉ 0.13 + ԕǍᖒ᮹ aᵲ⊹ 0.06 × ᖙᇡ᫵ᗭ ⧕ݚእᮉ 0.1

= 0.200

ᅙ ᩑǍᨱᕽ Table 5᪡ zᯕ 3D ༉ߙย ᖝᕽᄥ ᖁ⪙ḡᙹෝ

ᔑᱶ⦽đŝ2D ౩ᯕᱡᜅ⋱թa0.320, ᜅ▭౩᪅እᱥᖝᕽa

0.298ಽ ᔑᱶࡹᨕ ḡ܆⩶ Ǖᔎ ಽᅨ᮹ ಽ⍍ᩢᩎ 3₉ᬱ ༉ߙย

ᖝᕽಽእƱᱢᱢ⧊⦽äᮝಽᇥᕾࡹᨩᮝ໑, Ųݡᩎ3D ౩ᯕᱡ

ᜅ⋱թ(0.200)᪡౩ᯕᱡǍ᳑Ųᖝᕽ(0.183)۵ᱢ⧊⦹ḡᦫᮡäᮝ ಽᇥᕾࡹᨩ݅. ঑௝ᕽᅙᩑǍᨱᕽ۵2D ౩ᯕᱡᖝᕽ᪡ᜅ▭౩᪅

እᱥ ⋕ີ௝ ᖝᕽෝ ḡ܆⩶ Ǖᔎ ಽᅨ᮹ ᖝᕽ ᜽ᜅ▽ᮝಽ 1₉

ᖁᱶ⦹ᩡ݅.

4. ⩥ᰆᝅ⨹ᮥ☖⦽3₉ᬱ༉ߙยᖝᕽ᮹ᖒ܆ᇥᕾ

4.1 ਆഓߑૈणୢবছଭ3ఙ଀஺෴ࡦ܄ࠫ෮ୋഓਆൈ

ᅙᩑǍᨱᕽ۵ᦿᕽᖁ⪙ḡᙹᇥᕾᨱᕽaᰆ׳ᮡᱱᙹaᔑᱶࡽ

2D ౩ᯕᱡᖝᕽ᪡ᜅ▭౩᪅እᱥ⋕ີ௝ᖝᕽaᬕߑຝᱡᜅ▭౩᪅

እᱥ ⋕ີ௝ෝ ݡᔢᮝಽ ᝅᱽ ☁Ŗᔍ ᯲ᨦ⩥ᰆᨱᕽ ↽᳦ ᖝᕽ

ᖁᱶᮥ᭥⦽ᖒ܆▭ᜅ✙ෝᙹ⧪⦹ᩡ݅. ᜅ▭౩᪅እᱥ⋕ີ௝᮹

⩥ᰆᝅ⨹ᮡᙹࠥǭᗭᰍᝅᱽ☁Ŗ⩥ᰆᨱᕽᙹ⧪ࡹᨩᮝ໑, ᜅ▭౩ ᪅ እᱥ ⋕ີ௝ෝ Ǖᔎʑ᮹ ᬕᱥᕾ ᔢ݉ᨱ ᖅ⊹⦹Ł ᙹ⠪ᖁᮥ

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Fig. 4. Stereo Vision Based 3D Terrain Modeling at Earth-Work Environment

ʑᵡᮝಽ30ࠥԕಅ݅ᅕ۵ႊ⨆ᮝಽᖅ⊹⦹ᩡ݅. ᜅ▭౩᪅እᱥ

⋕ີ௝᮹↍ᩢ⧕ᔢࠥ۵640×480 ⪵ᗭᯕ໑, ⫮ाࡽḡၹ⩶ᔢᯕၙ

ḡ۵ᅙᩑǍᨱᕽ}ၽࡽ3D ༉ߙยᗭ⥥✙ᭉᨕෝᯕᬊ⦹ᩍ3₉ᬱ

ᩢᔢᮝಽ༉ߙย⦹ᩡ݅. ᅙᩑǍᨱᕽ۵ᜅ▭౩᪅እᱥ⋕ີ௝ᩢᔢ ᮹ ᇥᕾᮥ ᭥⦹ᩍ Visual C++ 2008 ⍕❭ᯝ్(compiler) ॒ᮥ

ᯕᬊ⦹ᩍᜅ▭౩᪅እᱥᩢᔢ᮹3₉ᬱ༉ߙยᗭ⥥✙ᭉᨕෝǍ⩥⦹

ᩡᮝ໑, Fig. 4۵ᯕ్⦽ŝᱶᮥ☖⧕☁Ŗ⩥ᰆ2₉ᬱᜅ▭౩᪅

ᩢᔢᮥ 3₉ᬱ ḡ⩶ᮝಽ ༉ߙย⦽ äᯕ݅.

☁Ŗᔍ᯲ᨦ⩥ᰆᨱᕽᜅ▭౩᪅እᱥ⋕ີ௝ෝᯕᬊ⦹ᩍḡ⩶ᮥ

3₉ᬱᮝಽ༉ߙย⦽đŝᱥℕᩢᔢᨱᕽיᯕᷩaኩჩ⦹íၽᔾ⦹

Łᯩᮭᮥၽč⧁ᙹᯩ݅. ☁Ŗ⩥ᰆᨱᕽᙹḲࡽ3₉ᬱ༉ߙย

đŝ᮹יᯕᷩ➉▕ᮥᇥᕾ⦽đŝ, ⮺ŝᦵᕾᮝಽǍᖒࡽᯝၹ

ḡၹᮡݡᇡᇥיᯕᷩၽᔾኩࠥ۵ᔢݚ⯩ԏᮝӹᔑၽᱢᮝಽၽᔾ

⦹۵Ğ⨆ᯕᯩᮝ໑, ᧝ᱢࡽ⎹Ⓧญ✙⮥šᯕӹḡ⢽ᙹෝ↍ᩢ⦽

Ğᬑ⢽໕ᔢݚᩢᩎᨱᕽฯᮡיᯕᷩaၽᔾ⦹Łḡၹŝษ₍aḡ ಽɪĊ⦽݉₉aᯩᮥĞᬑᨱᵝಽၽᔾ⦹۵Ğ⨆ᮥᅕᯙ݅. đುᱢ ᮝಽ☁Ŗᔍ᯲ᨦ⩥ᰆ᮹3₉ᬱᜅ▭౩᪅ๅ⋎ᩢᔢᮡיᯕᷩ౩ᄉᯕ

ԏᮡ⠙ᯕӹᱥၹᱢᮝಽⓍŁ᯲ᮡיᯕᷩaၽᔾ⦹໑, ᯕߑᯕ░ෝ

ᯕᬊ⦹ᩍᔝb฾ᮥᔾᖒ⧁Ğᬑיᯕᷩಽᯙ⧕ḡၹ⩶ᔢᯕ᪽łࢁ

a܆ᖒᯕᯩʑভྙᨱၹऽ᜽יᯕᷩᱽÑ᦭Łญ᷹ᮥ☖⧕ᱽÑࡹ

ᨕ᧝ ⦹۵ äᮝಽ ᇥᕾࡹᨩ݅(Yoo et al., 2009).

ᅙᩑǍᨱᕽ۵ᝅᱽᜅ▭౩᪅እᱥ⋕ີ௝ᨱ᮹⧕⊂ᱶࡽÑญ

sŝᝅ⊂ᮥ☖⧕ᕽ᨜ᮡÑญs᮹እƱෝ☖⧕ᜅ▭౩᪅እᱥ

⋕ີ௝᮹ᱶ⪶ࠥෝ⊂ᱶ⦹ᩡ݅. ⊂ᱶႊჶᮡ7}ᗭ᮹↍ᩢᩢᩎᨱ

ྕ᯲᭥ಽ 5~9}᮹ ༊⢽ྜྷᮥ ᖅ⊹⦹Ł ḡ⩶ŝ ༊⢽ྜྷᮥ ↍ᩢ⦽

⬥☁▙ᜅ▭ᯕᖹ(total station)ᮥᯕᬊ⦹ᩍ⊂ᱶࡽb༊⢽ྜྷʭḡ ᮹ᝅᱽÑญ᪡ᜅ▭౩᪅እᱥᗭ⥥✙ᭉᨕᔢᨱᕽĥᔑࡽÑญ᮹

⠙₉ෝ ⊂ᱶ⦹ᩡ݅(Table 6).

᭥᮹ᝅ⨹ႊჶᮥ☖⧕⫮ा⦽ⅾ48}᮹ᝅ⊂ߑᯕ░ෝʑၹᮝಽ

ᝅ⊂Ñญ᪡⊂ᱶÑญe⠙₉ෝᇥᕾ⦽đŝ᪅₉⠪Ɂᮡ0.041ၙ░

ᯕŁ↽ݡ᪅₉۵0.160ၙ░ᯙäᮝಽᇥᕾࡹᨩ݅. ⦽⠙, ᜅ▭౩᪅

እᱥߑᯕ░᮹᪅₉۵༊⢽ྜྷʭḡ᮹Ñญaມᨕḩᙹಾᱱḥᱢᮝ ಽ ᪅₉ ⡎ᯕ ᷾a⦹໑ Ñญ ݡእ ᪅₉ እ۵ ᮁḡࡹ۵ äᮝಽ

ᇥᕾࡹᨩ݅(Yu et al., 2009).

4.2 2D ߑଲୠবছଭ3ఙ଀஺෴ࡦ܄ࠫ෮ୋഓਆൈ

2D ౩ᯕᱡ ᖝᕽ᮹ ⩥ᰆ ▭ᜅ✙۵ Fig. 5᮹ (a)᪡ zᯕ 2D ౩ᯕᱡᜅ⋱թ(LMS-111)᪡ᱶᗮ⫭ᱥʑǍෝᝅᱽǕᔎʑᬕᱥᕾ

अ⠙ᨱᖅ⊹⦹Ł, ᖝᕽෝᱶᗮ⫭ᱥ⦹ᩍ3₉ᬱߑᯕ░ෝ⫮ा⦹۵

ႊ᜾ᮝಽᙹ⧪ࡹᨩ݅. ౩ᯕᱡᖝᕽߑᯕ░۵ᜅ▭౩᪅እᱥߑᯕ░

᪡۵ݍญᜅ⋵໕ᯕᩑᗮᱡᰆࡹ۵ߑᯕ░ᯕ໑, ⧕ݚᝅ⨹ᨱᕽ۵

᧞ 12ᇥe᮹ ߑᯕ░ෝ ⫮ा⦹ᩡ݅. ⫮ाࡽ ᩢᔢᮡ ᅙ ᩑǍᨱᕽ

}ၽࡽᗭ⥥✙ᭉᨕෝᯕᬊ⦹ᩍ3₉ᬱ⠪໕ᮝಽ༉ߙย⦹Ł, יᯕᷩ

ᙹᵡŝ⥥ಽᖙᝒ᮹⣩ḩᱶࠥෝ❱ᄥ⦹ᩍ2D ౩ᯕᱡᖝᕽ᮹☁Ŗ⩥

ᰆᱢ⧊ᖒᇥᕾᮥᙹ⧪⦹ᩡ݅. ᅙᩑǍᨱᕽ۵2D ౩ᯕᱡᜅ⋱թ

ḡ⩶ ߑᯕ░᮹ ᇥᕾᨱ ᯩᨕ Microsoft㫝᮹ Visual C++ 2008

⍕❭ᯝ్(compiler) ॒ᮥᯕᬊ⦹ᩍ 3₉ᬱ༉ߙย ᗭ⥥✙ᭉᨕෝ

Ǎ⩥⦹ᩡᮝ໑, Fig. 6ᮡ☁Ŗ⩥ᰆ2D ౩ᯕᱡᜅ⋱թߑᯕ░ෝ

3₉ᬱ ḡ⩶ᮝಽ ༉ߙย⦽ äᯕ݅.

☁Ŗᔍ ᯲ᨦ⩥ᰆᨱᕽ 2D ౩ᯕᱡ ᜅ⋱թෝ ᯕᬊ⦹ᩍ ḡ⩶ᮥ

3₉ᬱᮝಽ༉ߙย⦽đŝᜅ▭౩᪅እᱥ⋕ີ௝᪡۵ݍญיᯕᷩ۵

ᱥ⩡ၽčࡹḡᦫᦹᮝ໑, Fig. 6ŝzᯕᱥၹᱢᯙ3₉ᬱ༉ߙย

⣩ḩࠥๅᬑᬑᙹ⦹ᩡ݅. Fig. 6ᨱᕽǕᔎʑᵝᄡᔍ௭᮹⩶ᔢŝ

⊂పʑʑ᮹ ⩶ᔢᯕ ༉ࢱ૽ಘ⦽ ⩶ᔢᮝಽ á⇽ࢉᮥ⪶ᯙ⧁ ᙹ

ᯩᮝ໑, 3₉ᬱ⡍ᯙ✙᮹ၡࠥ۵݉᭥eĊʑᵡᮝಽᅝভᜅ▭౩᪅

እᱥ ↽ݡ ⧕ᔢࠥᨱ እ⧕ እƱᱢ ԏᮡ äᮝಽ ⪶ᯙࡹᨩ݅.

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(a) 2D Laser Sensor and Rotating

Equipment (b) 3D Terrain Data Acquisition Table 6. Accuracy Test Data of Stereo Vision [Unit:m]

Img. Target No 1 2 3 4 5 6 7 8 9

1

A 3.770 5.420 7.280 9.170 11.140

B 3.809 5.469 7.310 9.225 11.157

Difference 0.039 0.049 0.030 0.055 0.017

2

A 3.770 3.850 3.960 5.430 7.270 7.730 8.020 11.170 12.100

B 3.815 3.892 4.027 5.469 7.291 7.718 8.044 11.202 11.940

Difference 0.045 0.042 0.067 0.039 -0.021 -0.012 0.024 0.032 -0.160

3

A 4.610 4.710 5.510 5.940 8.610 9.300

B 4.644 4.649 5.554 6.031 8.690 9.344

Difference 0.034 -0.061 0.044 0.091 0.080 0.044

4

A 4.230 4.300 4.880 5.960 5.990 6.200 7.580

B 4.250 4.362 4.958 5.990 6.060 6.189 7.600

Difference 0.020 0.062 0.078 0.030 0.070 -0.011 0.020

5

A 3.930 4.180 5.020 5.460 5.850 7.590 7.700

B 3.993 4.184 5.104 5.537 5.902 7.648 7.793

Difference 0.063 0.004 0.084 0.077 0.052 0.058 0.093

6

A 4.650 5.430 5.620 6.050 6.160 7.500 7.830

B 4.719 5.455 5.762 6.178 6.229 7.614 7.983

Difference 0.069 0.025 0.142 0.128 0.069 0.114 0.153

7

A 3.370 3.640 4.190 5.300 5.620 6.940 6.774

B 3.425 3.633 4.268 5.330 5.609 6.951 6.722

Difference 0.055 -0.007 0.078 0.030 -0.011 0.011 -0.052

Description Statistic Std. Error

Mean 0.0412 0.0076

95% Confidence Interval for Mean Lower 0.0259

Upper 0.0566

Variance 0.0030

Standard deviation 0.0529

Minimum -0.1600

Maximum 0.1530

Significance Probability 0.000

A : calculated distance at 3D model B : actual distance measured by total station

2D ౩ᯕᱡᜅ⋱թ᮹ᱶ⪶ᖒ⊂ᱶႊჶᮡᜅ▭౩᪅እᱥŝ࠺ᯝ⦹

í☁▙ᜅ▭ᯕᖹ⊂పᮥ☖⧕⊂ᱶࡹᨩᮝ໑, 2D ౩ᯕᱡᜅ⋱թ᮹

ᬱᱱŝ༊⢽ྜྷe᮹ᝅᱽÑญ᪡3₉ᬱ ḡ⩶ߑᯕ░ᨱᕽ2D ౩ᯕᱡ

ᜅ⋱թ᪡༊⢽ྜྷe᮹Ñญ18}ᗭෝእƱ⦹ᩍ᪅₉ෝ⊂ᱶ⦹ᩡ݅.

ᔢʑᝅ⨹ႊჶᮥ☖⧕⫮ा⦽ᝅ⊂ߑᯕ░ෝʑၹᮝಽᝅ⊂Ñญ᪡

⊂ᱶÑญ e ᪅₉ෝ ᇥᕾ⦽ đŝ Table 7ŝ zᯕ ⠪Ɂ ᪅₉۵

0.003 ၙ░ᯕŁ ↽ݡ ᪅₉۵ 0.023 ၙ░ಽ ĥᔑࡹᨕ ᜅ▭౩᪅

እᱥᨱ እ⧕ እƱᱢ ᱶ⪶ࠥa ᬑᙹ⦽ äᮝಽ ᇥᕾࡹᨩ݅. ੱ⦽

2D ౩ᯕᱡᜅ⋱թߑᯕ░᮹᪅₉۵ᜅ▭౩᪅እᱥ⋕ີ௝᪡ษ₍a

(11)

Fig. 6. 2D Laser Scanner Based 3D Terrain Modeling

Table 7. Accuracy Test Data of 2D Laser Scanner [Unit:m]

Img. Target No. 1 2 3 4 5 6 7 8 9

1

A 4.462 5.189 6.842 8.759 10.485 12.568 14.896 18.963 22.369

B 4.459 5.186 6.848 8.750 10.489 12.565 14.906 18.971 22.346

Difference 0.003 0.003 -0.006 0.009 -0.004 0.003 -0.010 -0.008 0.023

2

A 3.956 4.661 6.624 7.845 9.966 11.987 13.855 17.915 20.112

B 3.950 4.656 6.625 7.840 9.956 11.978 13.853 17.922 20.100

Difference 0.006 0.005 -0.001 0.005 0.010 0.009 0.002 -0.007 0.012

Description Statistic Std. Error

Mean 0.0030 0.0011

95% Confidence Interval for Mean Lower -0.0011

Upper 0.0071

Variance 0.0000

Standard deviation 0.0082

Minimum -0.0100

Maximum 0.0230

Significance Probability 0.041

A : calculated distance at 3D model B : actual distance measured by total station

ḡಽ ༊⢽ྜྷʭḡ᮹ Ñญa ມᨕḩᙹಾ ᱱ₉ ᪅₉ ⡎ᯕ ⍅ḡ໑

Ñญ ݡእ ᪅₉ እ۵ ᮁḡࡹ۵ äᮝಽ ᇥᕾࡹᨩ݅.

4.3 ෮ୋਓ෠էրंজଡധ෉3ఙ଀஺෴ࡦ܄ࠫଡ଍

෉ౖୡবছট୨

ᅙᩑǍᨱᕽ۵ᝅᱽ☁Ŗᔍ᯲ᨦ⩥ᰆ᮹3₉ᬱḡ⩶༉ߙยᮥ

᭥⦹ᩍᜅ▭౩᪅እᱥ⋕ີ௝᪡2D ౩ᯕᱡᖝᕽ᮹3₉ᬱḡ⩶

ߑᯕ░ෝ ⫮ा⦹ᩡᮝ໑, bᖝᕽ᮹ಽᬑ(raw) ߑᯕ░ෝ3₉ᬱᮝಽ

༉ߙย⦹Ł༉ߙย⣩ḩ⊂໕ŝ༉ߙยᱶ⪶ᖒ⊂໕ᨱᕽá☁⦹ᩡ

݅. ຝᱡᜅ▭౩᪅እᱥ⋕ີ௝ෝᯕᬊ⦹ᩍ☁Ŗᔍ᯲ᨦḡ⩶ᮥ

3₉ᬱ ༉ߙย⦽ đŝ, ᝅԕ᮹ ᯙŖ Ǎ᳑ྜྷᮥ ݡᔢᮝಽ ↍ᩢ⦽

༉ߙยđŝᅕ݅۵እƱᱢᬑᙹ⦹ᩡᮝӹ, ᱥၹᱢᮝಽⓍŁ᯲ᮡ

יᯕᷩaၽᔾ⦹ᩡ݅. ☁Ŗᔍ᯲ᨦ⪹Ğ᮹3₉ᬱ༉ߙยđŝᨱᕽ

יᯕᷩ ߑᯕ░a ᳕ᰍ⧁ Ğᬑ ᩩᔢࡹ۵ ྙᱽᱱᮡ ḡ⩶᮹ ᇡ⦝

(volume)ĥᔑᨱ᪅₉aၽᔾ⦹ᩍ☁Ŗప᮹ĥᔑᨱᩢ⨆ᮥၙ⊹Ł, יᯕᷩaᯩ۵ḡ⩶ᮡ⦝௝ၙऽ⩶┽᮹ๅ᜽aᔾᖒࡹʑভྙᨱ

ḡ܆⩶ Ǖᔎ᜽ᜅ▽ᯕ ᯕෝ ᰆᧁྜྷಽ ᯙ᜾⧁ ᙹ ᯩ݅۵ ᱱᯕ݅.

঑௝ᕽ ᜅ▭౩᪅ እᱥ ⋕ີ௝ෝ ḡ܆⩶ Ǖᔎ ಽᅨ᮹ ⪹Ğᯙ᜾

ᖝᕽಽ⪽ᬊ⦹ʑ᭥⧕ᕽ۵vಆ⦽יᯕᷩ⦥░ย᦭Łญ᷹ᯕ᫵Ǎ

ࡽ݅. ⦽⠙, 2D ౩ᯕᱡ ᜅ⋱թෝ ᯕᬊ⦹ᩍ ☁Ŗᔍ ᯲ᨦḡ⩶ᮥ

༉ߙย⦽đŝᨱᕽ۵יᯕᷩaᱥ⩡ၽčࡹḡᦫᦹŁᱥၹᱢᯙ

3₉ᬱ༉ߙย⣩ḩࠥᜅ▭౩᪅እᱥ⋕ີ௝ᨱእ⧕ᬑᙹ⦹ᩡ݅.

⦽⠙, ᝅᱽ ☁Ŗᔍ ⩥ᰆᨱᕽ ⊂ᱶ ჵ᭥ ԕᨱ ྕ᯲᭥ಽ ༊⢽ྜྷ

(target)ᮥᖅ⊹⦹Ł, ᬱᱱŝ༊⢽ྜྷᔍᯕ᮹Ñญෝ⊂ᱶ⦽đŝ

ᱥၹᱢᮝಽ2D ౩ᯕᱡᜅ⋱թ᮹ᱶ⪶ᖒᯕᜅ▭౩᪅እᱥ⋕ີ௝ᨱ

እ⧕ ᬵ॒⯩ ᬑᙹ⦹ᩡ݅. ᜅ▭౩᪅ እᱥ ⋕ີ௝۵ 2D ౩ᯕᱡ

ᜅ⋱թᨱእ⧕ḡ⩶᮹↍ᩢᗮࠥaᬵ॒⯩ᝁᗮ⦹Ł, 3₉ᬱ⡍ᯙ✙

᮹ ᔪᔢ ᱶᅕʭḡ ⧉̹ ⫮ा⧁ ᙹ ᯩ݅۵ ᰆᱱᯕ ᯩḡอ Õᖅ

ᯱ࠺⪵ᰆእ۵ᝁ഑ᖒ׳ᮡᵝᄡ⪹Ğᯙ᜾ʑᚁᮥ⦥᫵ಽ⦽݅.

ᅙᩑǍᨱᕽ۵Õᖅ᯲ᨦ⪹Ğᨱᱢ⧊⦽3₉ᬱ༉ߙยᖝᕽಽ2D ౩ᯕᱡ ᜅ⋱թෝ ↽᳦ ᖁᱶ⦹ᩡ݅.

(12)

5. đು

ᅙᩑǍ۵⩥ᰍʭḡ}ၽࡽ3₉ᬱ᯲ᨦ⪹Ğᖝᝒʑᚁᇥᕾᮥ

☖⧕ḡ܆⩶Ǖᔎಽᅨ᮹3₉ᬱḡ⩶༉ߙยᨱᱢ⧊⦽ᖝᕽෝᖁᱶ⦹Ł

ᝅᱽ☁Ŗᔍ᯲ᨦ⪹Ğ᮹3₉ᬱ༉ߙย⩥ᰆᝅ⨹ᮥ☖⧕ᖁᱶᖝᕽ᮹

ᖒ܆ᮥ á᷾⦹ʑ ᭥⦽ ᩑǍಽ៉, ᅙ ᩑǍ᮹ đುᮡ ݅ᮭŝ z݅.

(1) ᅙ ᩑǍᨱᕽ۵ ᖁ⧪ }ၽࡽ ᪥ᱥ ᯱ࠺⪵ ႊ᜾᮹ Ǖᔎ ಽᅨ

}ၽ⩥⫊ၰ3₉ᬱḡ⩶༉ߙยᖝᕽʑᚁᮥᇥᕾ⦹ᩡ݅. ə

đŝၙǎᨱᕽ}ၽࡽCMU᮹᪥ᱥᯱ࠺⪵Ǖᔎʑ۵Ła᮹

3D ౩ᯕᱡ ᜅ⋱թ aĊᯕ ᗭ᫵ࡹ۵ ྙᱽᱱᯕ ᯩᨩŁ ᯝᅙ

PWRI᮹᪥ᱥᯱ࠺⪵Ǖᔎಽᅨᮡ☁Ŗᔍ᯲ᨦḡ⩶1⫭↍ᩢษ

݅21Ⅹ᮹3D ༉ߙยᩑᔑ᜽eᯕᗭ᫵ࡹ۵ྙᱽᱱᯕᯩ۵

äᮝಽᇥᕾࡹᨩ݅. ᅙᩑǍᨱᕽ۵ᯕ᪡zᮡྙᱽᱱᇥᕾᮥ

☖⦹ᩍ Ğᱽᖒ, ᝁᗮᖒ ၰ ⊂ᱶჵ᭥, ᱶ⪶ᖒ, ᖅ⊹ ᬊᯕᖒ, ԕǍᖒ ॒ 5aḡ ⧎༊᮹ Łಅ᫵ᗭෝ ࠥ⇽⦹ᩡ݅.

(2) ᅙᩑǍᨱᕽ۵ḡ܆⩶Ǖᔎಽᅨ᮹3₉ᬱ༉ߙยᖝᕽᖁᱶᮥ

᭥⦹ᩍ⩥ᰍᔍᬊࡹ۵3₉ᬱ༉ߙยᖝᕽʑᚁ᮹✚Ḷၰᔍ᧲ᮥ

᳑ᔍ⦹ᩍᰆ݉ᱱᮥᇥᕾ⦽đŝ2D ౩ᯕᱡᖝᕽ᪡ᜅ▭౩᪅

እᱥᖝᕽaḡ܆⩶Ǖᔎಽᅨ᜽ᜅ▽ᨱᱢᬊa܆ᖒᯕ׳ᮡ

äᮝಽᇥᕾࡹᨩ݅. ⦽⠙AHP ᇥᕾႊჶᮥᯕᬊ⦽Łಅ᫵ᗭ

e᮹ᝮݡእƱđŝ, ᱶ⪶ᖒ᮹aᵲ⊹a0.49ಽaᰆ׳íӹ┡ԍ ᮝ໑, Ğᱽᖒᯕ0.24, ᝁᗮᖒၰ⊂ᱶჵ᭥a0.12, ᖅ⊹ᬊᯕᖒᯕ

0.09, ԕǍᖒ0.06 ᙽᮝಽᇥᕾࡹᨩ݅. ⦽⠙, ᅙᩑǍᨱᕽ۵Łಅ

᫵ᗭԕᖙᇡŁಅ᫵ᗭᨱݡ⦹ᩍbݡᦩ᮹⧕ݚᩍᇡෝá☁⦹ᩡ

Ł, ᯕෝ☖⦽3D ༉ߙยᖝᕽᄥᖁ⪙ḡᙹෝᔑᱶ⦽đŝ2D ౩ᯕᱡᜅ⋱թa0.320, ᜅ▭౩᪅እᱥᖝᕽa0.298ಽᔑᱶࡹᨕ

ḡ܆⩶Ǖᔎಽᅨ᮹ಽ⍍ᩢᩎ3₉ᬱ༉ߙยᖝᕽಽᱢ⧊⦽äᮝಽ

ᇥᕾࡹᨩᮝ໑, Ųݡᩎ3D ౩ᯕᱡᜅ⋱թ(0.200)᪡౩ᯕᱡǍ᳑

Ų ᖝᕽ(0.183)۵ ᱢ⧊⦹ḡ ᦫᮡ äᮝಽ ᇥᕾࡹᨩ݅.

(3) ᅙᩑǍᨱᕽ۵2᳦᮹3₉ᬱ༉ߙยᖝᕽෝᝅᱽ☁Ŗᔍ᯲ᨦ⩥

ᰆᨱᱢᬊ⦹ᩍ༉ߙย▭ᜅ✙ෝᙹ⧪⦹ᩡŁ, ⧕ݚᖝᕽಽᇡ░

⫮ाࡽߑᯕ░ෝᇥᕾ⦹ᩍ3₉ᬱ༉ߙย⣩ḩၰᱶ⪶ᖒ⠪aෝ

ᙹ⧪⦽đŝ, 2D ౩ᯕᱡᜅ⋱թ᮹3₉ᬱ༉ߙย⣩ḩŝᱶ⪶ᖒ ᯕᜅ▭౩᪅እᱥ⋕ີ௝ᨱእ⧕༉ࢱᬵ॒⯩ᬑᙹ⦽äᮝಽ

ᇥᕾࡹᨩ݅. ✚⯩ ᝅᱽ ☁Ŗᔍ ᯲ᨦ⩥ᰆᨱ ᱢᬊࢁ Ğᬑ 2D ౩ᯕᱡᜅ⋱թ᮹༉ߙยđŝᨱᕽ۵יᯕᷩaᱥ⩡ၽčࡹḡ

ᦫᦹᮝ໑ๅᬑᱶ⪶⦽3₉ᬱ༉ߙยđŝෝᅕᰆ⦹۵äᮝಽ

ᇥᕾࡹᨩ݅. ⨆⬥ ḡ܆⩶ Ǖᔎ ಽᅨᨱ 2D ౩ᯕᱡ ᜅ⋱թෝ

ᱢᬊ⦹ᩍ 3₉ᬱ ༉ߙย đŝෝ ᨜ʑ ᭥⧕ᕽ۵ ᱶၡ⦽ ᱶᗮ

⫭ᱥʑǍ(㝍㜮)aᱽ᯲ࡹᨕ᧝⦹໑, Ǖᔎಽᅨᵝᄡḡ⩶ᮥ

Ɂᯝ⦽ ၡࠥಽ ᜅ⋱ܾ⧁ ᙹ ᯩ۵ ᭥⊹ᨱ ᖅ⊹ࡹᨕ᧝ ⦽݅.

qᔍ᮹ɡ

ᅙᩑǍ۵2010֥ࠥƱᮂŝ⦺ʑᚁᇡ᮹ᰍᬱᮝಽ⦽ǎᩑǍᰍ݉

᮹ ʑⅩᩑǍᔍᨦ ḡᬱ(2010-0026774)ᮥ ၼᦥ ᙹ⧪ࡹᨩ᜖ܩ݅.

ᅙᩑǍ۵ǎ☁Ʊ☖ᇡÕᖅʑᚁᩑǍᔍᨦ᮹ᩑǍእḡᬱ(06℉݉

ᮖ⧊C01)ᨱ ᮹⧕ ᙹ⧪ࡹᨩ᜖ܩ݅.

ᅙ ᩑǍ۵ ᯙ⦹ݡ⦺Ʊ ᩑǍእ ḡᬱᨱ ᮹⧕ ᙹ⧪ࡹᨩ᜖ܩ݅.

References

Cannon, H. (1999). Extended earthmoving with an autonomous excavator, Masters Dissertation, Carnegie Mellon Robotics Institute, CMU-RI-TR-99-10, USA.

Kim, J. H. and Seo, J. W. (2011). “BIM based intelligent excavation system.” Korean J. of BIM, KIBIM, Vol. 1, No. 1, pp. 1-5 (in Korean).

MOCT (2011). Statistics of specialty contractor constr-uction, Avaslable at: http://stat.mltm.go.kr/portal/cate/viewChk.do?hRsId=

39, (Accessed: April 16, 2013) (in Korean).

Seo. J. W., Park, C. W. and Jang, D. S. (2007). “Development of intelligent excavating system - Introduction of research center.”

2007 Proc. of KICEM, pp. 197-204 (in Korean).

Stentz, A., Bares, J., Singh, S. and Rowe, P. (1999). “A robotic excavator for autonomous truck loading.” Autonomous Robots, Kluwer Academic Publishers, Vol. 7, No. 2, pp. 175-186.

Yamamoto, H., Uesaka, K., Ishimaisu, Y., Yamaguchi, T., Aritomi, K.

and Tanaka, Y. (2006b). “Introduction to the general technology development project: Research and development of advanced execution technology by remote control robot and information technology.” 2006 Proc. of ISARC, pp. 24-29.

Yamamoto, H., Ishimatsu, Y., Ageishi, S., Ikeda, N., Endo, K., Masuda, M., Uchida, M. and Yamaguchi, H. (2006a). “Example of experimental use of 3D measurement system for construction robot based on component design concept.” 2006 Proc. of ISARC, pp. 252-257.

Yamamoto, H., Moteki, M., Shao, H., Ootuki, T., Kanazawa, H. and Tanaka, Y. (2009). “Basic technology toward autonomous hydraulic excavator.” 2009 Proc. of ISARC, pp. 288-295.

Yoo, H. S., Kim, Y. S. and Han, S. W. (2009). “Development of the noise elimination algorithm of stereo-vision images for 3D terrain modeling.” Korean J. of Cons. Eng. and Mgmt., KICEM, Vol. 10, No. 2, pp. 145-154 (in Korean).

Yu, B. I., Yoo, H. S., Kim, Y. S., Seo, J. W. and Han, S. W. (2009).

“Application of appropriate technologies to 3D local terrain modeling in real-time for intelligent excavating system (IES).”

2009 Proc. of ISARC, pp. 357-364.