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This paper presents a novel method for qualitative mapping of large scalespaces. The proposed framework makes use of a graphical representation of theworld in order to build a map consisting of qualitative constraints on thegeometric relationships between landmark triplets. A novel measurement methodbased on camera imagery is presented which extends previous work from the fieldof Qualitative Spatial Reasoning. Measurements are fused into the map using adeterministic, iterative graph update. A Branch-and-Bound approach is taken tosolve a set of non-convex feasibility problems required for generating on-linemeasurements and off-line operator lookup tables. A navigation approach fortravel between distant landmarks is developed, using estimates of the RelativeNeighborhood Graph extracted from the qualitative map in order to generate asequence of landmark objectives based on proximity. Average and asymptoticperformance of the mapping algorithm is evaluated using Monte Carlo tests onrandomly generated maps, and data-driven simulation results are presented for arobot traversing the Jet Propulsion Laboratory Mars Yard while building arelational map. Simulation results demonstrate an initial rapid convergence ofqualitative state estimates for visible landmarks, followed by a slow taperingas the remaining ambiguous states are removed from the map.

Qualitative Relational Mapping and Navigation for Planetary Rovers