TY - GEN
T1 - Real-time object recognition and modeling for heavy-equipment operation
AU - Son, Hyojoo
AU - Kim, Changwan
AU - Kim, Hyoungkwan
AU - Choi, Kwang Nam
AU - Jee, Jeong Min
PY - 2008
Y1 - 2008
N2 - Recognition of free-form objects located in environments that are difficult to characterize or are constantly changing is critical in providing interactive background information for construction worksite modeling. It also allows for accurate, efficient, and autonomous operation of heavy equipment in a broad range of construction tasks. This paper presents a realtime process for 3D modeling of a construction worksite scene and focuses on modeling of the target object via flash LADAR, which could be applied to autonomous heavy-equipment operation. The proposed method consists of three steps: noise reduction, object extraction, and 3D model generation. The whole process is fully automatic and is performed in nearly real time. The method was validated in field experiments with actual construction objects. The results show that the proposed method effectively recognizes construction objects, which could be used to enhance efficiency and productivity in the autonomous operation of heavy equipment.
AB - Recognition of free-form objects located in environments that are difficult to characterize or are constantly changing is critical in providing interactive background information for construction worksite modeling. It also allows for accurate, efficient, and autonomous operation of heavy equipment in a broad range of construction tasks. This paper presents a realtime process for 3D modeling of a construction worksite scene and focuses on modeling of the target object via flash LADAR, which could be applied to autonomous heavy-equipment operation. The proposed method consists of three steps: noise reduction, object extraction, and 3D model generation. The whole process is fully automatic and is performed in nearly real time. The method was validated in field experiments with actual construction objects. The results show that the proposed method effectively recognizes construction objects, which could be used to enhance efficiency and productivity in the autonomous operation of heavy equipment.
UR - http://www.scopus.com/inward/record.url?scp=77949492795&partnerID=8YFLogxK
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U2 - 10.3846/isarc.20080626.232
DO - 10.3846/isarc.20080626.232
M3 - Conference contribution
AN - SCOPUS:77949492795
SN - 9789955283294
T3 - ISARC 2008 - Proceedings from the 25th International Symposium on Automation and Robotics in Construction
SP - 232
EP - 237
BT - ISARC 2008 - Proceedings from the 25th International Symposium on Automation and Robotics in Construction
PB - Vilnius Gediminas Technical University
T2 - 25th International Symposium on Automation and Robotics in Construction, ISARC 2008
Y2 - 26 June 2008 through 29 June 2008
ER -