Most recent robotic systems, capable of exploring unknown environments, use topological structures (graphs) as a spatial representation.
Localization can be done by deriving an estimate of the global pose from landmark information. In this case navigation is
tightly coupled to metric knowledge, and hence the derived control method is mainly pose-based. Alternative to continuous
metric localization, it is also possible to base localization methods on weaker constraints, e.g. the similarity between images
capturing the appearance of places or landmarks. In this case navigation can be controlled by a homing algorithm. Similarity
based localization can be scaled to continuous metric localization by adding additional constraints, such as alignment of
depth estimates.
We present a method to scale a similarity based navigation system (the view-graph-model) to continuous metric localization.
Instead of changing the landmark model, we embed the graph into the three dimensional pose space. Therefore, recalibration
of the path integrator is only possible at discrete locations in the environment. The navigation behavior of the robot is
controlled by a homing algorithm which combines three local navigation capabilities, obstacle avoidance, path integration,
and scene based homing. This homing scheme allows automated adaptation to the environment. It is further used to compensate
for path integration errors, and therefore allows to derive globally consistent pose estimates based on “weak” metric knowledge,
i.e. knowledge solely derived from odometry. The system performance is tested with a robotic setup and with a simulated agent
which explores a large, open, and cluttered environment.
Keywords Autonomous map building - Simultaneous localization and mapping - Cognitive mapping
This work is part of the GNOSYS (FP6-003835-GNOSYS) project, supported by the European Commission.