Factoring the Mapping Problem

To solve the mapping problem, an autonomous robot explores an unknown environment and uses its own observations to build a useful map. Maps have a variety of different uses, including route planning, local motion control with hazard avoidance, estimating distances and directions, localization, and place recognition. Although important progress has been made on the SLAM (simultaneous localization and mapping) problem within a single global frame of reference, metrical uncertainty can still accumulate over time, making it difficult to close large loops with confidence. In recent work to address this problem, we have revised the basic Spatial Semantic Hierarchy (SSH) to become the hybrid SSH, by defining a clean interface to the local perceptual map. We exploit the strengths of three different map representations to factor the mapping problem into three distinct sub-problems that can be solved reliably: By factoring the problem in this way, we can build an accurate global metrical map on the skeleton provided by the accurate global topological map. The factored problem also leads to a robust and useful map: the local metrical map is useful for place recognition and local motion control with hazard avoidance; the global topological map is useful for localization and route planning; and the global metrical map is useful for estimating distances and directions.


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