http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Extraction of Different Types of Geometrical Features from Raw Sensor Data of Two-dimensional LRF
염서군,무경,원조,한창수,Yan, Rui-Jun,Wu, Jing,Yuan, Chao,Han, Chang-Soo Institute of Control 2015 제어·로봇·시스템학회 논문지 Vol.21 No.3
This paper describes extraction methods of five different types of geometrical features (line, arc, corner, polynomial curve, NURBS curve) from the obtained raw data by using a two-dimensional laser range finder (LRF). Natural features with their covariance matrices play a key role in the realization of feature-based simultaneous localization and mapping (SLAM), which can be used to represent the environment and correct the pose of mobile robot. The covariance matrices of these geometrical features are derived in detail based on the raw sensor data and the uncertainty of LRF. Several comparison are made and discussed to highlight the advantages and drawbacks of each type of geometrical feature. Finally, the extracted features from raw sensor data obtained by using a LRF in an indoor environment are used to validate the proposed extraction methods.
Extraction of Different Types of Geometrical Features from Raw Sensor Data of Two-dimensional LRF
Rui-Jun Yan(염서군),Jing Wu(무경),Chao Yuan(원조),Chang-Soo Han(한창수) 제어로봇시스템학회 2015 제어·로봇·시스템학회 논문지 Vol.21 No.3
This paper describes extraction methods of five different types of geometrical features (line, arc, corner, polynomial curve, NURBS curve) from the obtained raw data by using a two-dimensional laser range finder (LRF). Natural features with their covariance matrices play a key role in the realization of feature-based simultaneous localization and mapping (SLAM), which can be used to represent the environment and correct the pose of mobile robot. The covariance matrices of these geometrical features are derived in detail based on the raw sensor data and the uncertainty of LRF. Several comparison are made and discussed to highlight the advantages and drawbacks of each type of geometrical feature. Finally, the extracted features from raw sensor data obtained by using a LRF in an indoor environment are used to validate the proposed extraction methods.