Kavraki Lab

Physical and Biological Computing

URL:

Keywords:

Abstract:

An important milestone for building affordable robots
that can become widely popular is to address robustly
the Simultaneous Localization and Mapping (SLAM) problem
with inexpensive, off-the-shelf sensors, such as monocular
cameras. These sensors, however, impose significant
challenges on SLAM procedures because they provide only
bearing data related to environmental landmarks. This paper
starts by providing an extensive comparison of different
techniques for bearing-only SLAM in terms of robustness under
different noise models, landmark densities and robot paths.
We have experimented in a simulated environment with a
variety of existing online algorithms including
Rao-Blackwellized Particle Filters (RB-PFs). Our experiments
suggest that RB-PFs are more robust compared to other
existing methods and run considerably faster. Nevertheless,
their performance suffers in the presence of outliers. In
order to overcome this limitation we proceed to propose an
augmentation of RB-PFs with: (a) Gaussian Sum Filters for
landmark initialization and (b) an online, unsupervised
outlier rejection policy. This framework exhibits impressive
robustness and efficiency even in the presence of outliers.