Since a RGB-D sensor provides rich information about the scene, various object recognition schemes and low-level image descriptors can be used to improve the SLAM performance. However, a cleaning robot, which is usually flat and thus the camera is close to the floor, usually only has a partial view of the objects in front of the camera; therefore, conventional object recognition schemes based on the complete view of objects are not suitable. To address this problem, we introduce a novel object surface recognition algorithm based on the proposed surface component ratio histogram (SCRH). SCRH is a surface descriptor which describes the geometrical shape of the partial view of the object. Without any pre-trained model of the objects, SCRH provides a way to deal with the unexpected objects which the robot encounters during the navigation. SCRH was evaluated using several objects lying on the floor of which the identities are not known in advance. The experimental results show that objects are successfully discriminated based on their surfaces and SCRH is robust for object surface recognition.
|Number of pages||8|
|Journal||International Journal of Control, Automation and Systems|
|Publication status||Published - 2019 Mar 1|
Bibliographical noteFunding Information:
Manuscript received February 7, 2018; revised July 15, 2018; accepted November 6, 2018. Recommended by Associate Editor Huaping Liu under the direction of Editor Euntai Kim. This research was supported by the (MOTIE) under the Industrial Foundation Technology Development Program, supervised by the (KEIT) (No. 10084589).
© 2019, ICROS, KIEE and Springer.
- Embedded system
- fast point feature histogram
- small mobile robots
- surface recognition
ASJC Scopus subject areas
- Control and Systems Engineering
- Computer Science Applications