Authors:
Younès Raoui
and
El Houssine Bouyakhf
Affiliation:
Mohammed V University, Morocco
Keyword(s):
Steerable Filters, Repeatability, Color, Detector Descriptor, SLAM, Mapping.
Related
Ontology
Subjects/Areas/Topics:
Engineering Applications
;
Image Processing
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Nonlinear Signals and Systems
;
Robotics and Automation
;
Signal Processing, Sensors, Systems Modeling and Control
;
Vision, Recognition and Reconstruction
Abstract:
In this paper, we propose a new detector descriptor for the visual salient points and use it for a monocular visual
Simultaneous Localization and Mapping (visualSLAM) application. Because in SLAM, the landmarks should
be indexed with distinctive features, we aim to build a detector descriptor insuring the invariance to the rotation
and the scale. First, the detector starts filtering an image with a steerable filter set, extracts Harris corners from
the convolved image, next it clusters these corners, then it calculates a resulting set of feature points. We show
that the repeatability of the detector is higher that other detectors like SIFT, SURF, CENSURE and BRISC.
In addition, we implement the descriptor using color attributes. We represent the color at the location of each
feature point with a pyramid characterized with many levels of quantization, and we calculate the entropy at
each level. We make a simulation of Visual SLAM with known correspondences using these features to prov
e
their efficiency in the localization and the map management of the robot.
(More)