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Learning view graphs for robot navigation
We present a purely vision-based scheme for learning a topological representation of an open environment. The system represents selected places by local views of the surrounding scene, and finds traversable paths between them. The set of recorded views and their connections are combined into a graph model of the environment. To navigate between views connected in the graph, we employ a homing strategy inspired by findings of insect ethology. In robot experiments, we demonstrate that complex visual exploration and navigation tasks can thus be performed without using metric information.
@article{155, title = {Learning view graphs for robot navigation}, journal = {Autonomous Robots}, abstract = {We present a purely vision-based scheme for learning a topological representation of an open environment. The system represents selected places by local views of the surrounding scene, and finds traversable paths between them. The set of recorded views and their connections are combined into a graph model of the environment. To navigate between views connected in the graph, we employ a homing strategy inspired by findings of insect ethology. In robot experiments, we demonstrate that complex visual exploration and navigation tasks can thus be performed without using metric information.}, volume = {5}, number = {1}, pages = {111-125}, organization = {Max-Planck-Gesellschaft}, school = {Biologische Kybernetik}, month = mar, year = {1998}, slug = {155}, author = {Franz, M. and Sch{\"o}lkopf, B. and Mallot, HA. and B{\"u}lthoff, HH. and}, month_numeric = {3} }