TY - JOUR
T1 - Tree-based search of the next best view/state for three-dimensional object reconstruction
AU - Vasquez-Gomez, J. Irving
AU - Sucar, L. Enrique
AU - Murrieta-Cid, Rafael
AU - Herrera-Lozada, Juan Carlos
N1 - Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by CONACYT for the project cátedra 1507.
Funding Information:
The authors would like to thank CONACYT. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by CONACYT for the project cátedra 1507.
Publisher Copyright:
© 2018, © The Author(s) 2018.
PY - 2018/2/13
Y1 - 2018/2/13
N2 - Three-dimensional models from real objects have many applications in robotics. To automatically build a three-dimensional model from an object, it is essential to determine where to place the range sensor in order to completely observe the object. However, the view (position and orientation) of the sensor is not sufficient, given that its corresponding robot state needs to be calculated. Additionally, a collision-free trajectory to reach that state is required. In this article, we directly find the state of the robot whose corresponding sensor view observes the object. This method does not require to calculate the inverse kinematics of the robot. Unlike previous approaches, the proposed method guides the search with a tree structure based on a rapidly exploring random tree overcoming previous sampling techniques. In addition, we propose an information metric that improves the reconstruction performance of previous information metrics.
AB - Three-dimensional models from real objects have many applications in robotics. To automatically build a three-dimensional model from an object, it is essential to determine where to place the range sensor in order to completely observe the object. However, the view (position and orientation) of the sensor is not sufficient, given that its corresponding robot state needs to be calculated. Additionally, a collision-free trajectory to reach that state is required. In this article, we directly find the state of the robot whose corresponding sensor view observes the object. This method does not require to calculate the inverse kinematics of the robot. Unlike previous approaches, the proposed method guides the search with a tree structure based on a rapidly exploring random tree overcoming previous sampling techniques. In addition, we propose an information metric that improves the reconstruction performance of previous information metrics.
KW - 3-D mapping
KW - 3-D reconstruction
KW - Next best view
KW - path planning
KW - rapidly exploring random tree
UR - http://www.scopus.com/inward/record.url?scp=85042764723&partnerID=8YFLogxK
U2 - 10.1177/1729881418754575
DO - 10.1177/1729881418754575
M3 - Artículo
AN - SCOPUS:85042764723
SN - 1729-8806
VL - 15
JO - International Journal of Advanced Robotic Systems
JF - International Journal of Advanced Robotic Systems
IS - 1
ER -