Improving visual SLAM by filtering outliers with the aid of optical flow
Author
Özaslan, Tolga
Advisor
Saranlı, Uluç
Date
2011Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
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Abstract
Simultaneous Localization and Mapping (SLAM) for mobile robots has been one
of the challenging problems for the robotics community. Extensive study of this
problem in recent years has somewhat saturated the theoretical and practical
background on this topic. Within last few years, researches on SLAM have been
headed towards Visual SLAM, in which camera is used as the primary sensor.
Superior to many SLAM application run with planar robots, VSLAM allows us to
estimate the 3D model of the environment and 6-DOF pose of the robot. Being
applied to robotics only recently, VSLAM still has a lot of room for improvement.
In particular, a common issue both in normal and Visual SLAM algorithms is
the data association problem. Wrong data association either disturbs stability or
result in divergence of the SLAM process. In this study, we propose two outlier
elimination methods which use predicted feature location error and optical flow
field. The former method asserts estimated landmark projection and its measurement
locations to be close. The latter accepts optical flow field as a reference
and compares the vector formed by consecutive matched feature locations; eliminates
matches contradicting with the local optical flow vector field. We have
shown these two methods to be saving VSLAM from divergence and improving
its overall performance. We have also described our new modular SLAM library,
SLAM++.