Abstract

In this paper we describe an approach that builds three dimensional maps using visual landmarks extracted
from images of an unmodified environment. We propose a solution to the Simultaneous Localization and
Mapping (SLAM) problem for autonomous mobile robots using visual landmarks. Our map is represented
by a set of three dimensional landmarks referred to a global reference frame, each landmark contains a visual
descriptor that partially differentiates it from others. Significant points extracted from stereo images are used
as natural landmarks, in particular we employ SIFT features found in the environment. We estimate both
the map and the path of the robot using a Rao-Blackwellized particle filter, thus the problem is decomposed
into two parts: one estimation over robot paths using a particle filter, and N independent estimations over
landmark positions, each one conditioned on the path estimate. We actively track visual landmarks at a local
neighbourhood and select only those that are more stable. When a visual feature has been observed from a
significant number of frames it is then integrated in the filter. By this procedure, the total number of landmarks
in the map is reduced, compared to prior approaches. Due to the tracking of each landmark, we obtain different
examples that represent the same natural landmark. We use this fact to improve data association. Finally,
efficient resampling techniques have been applied, which reduces the number of particles needed and avoids
the particle depletion problem.