Visual Bearing-Only Simultaneous Localization and Mapping with Improved Feature Matching
AMS(2007)
摘要
In this this paper, we present a solution to the simultaneous localization and mapping (SLAM) problem for a robot equipped
with a single perspective camera. We track extracted features over multiple frames to estimate the depth information. To represent
the joint posterior about the trajectory of the robot and a map of the environment, we apply a Rao-Blackwellized particle
filter. We present a novel method to match features using a cost function that takes into account differences between the
feature descriptor vectors as well as spatial information. To find an optimal matching between observed features, we apply
a global optimization algorithm. Experimental results obtained with a real robot show that our approach is robust and tolerant
to noise in the odometry information of the robot. Furthermore, we present experiments that demonstrate the superior performance
of our feature matching technique compared to other approaches.
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关键词
cost function,spatial information,global optimization,simultaneous localization and mapping
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