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We present a system to increase the performance of feature correspondence based alignment algorithms for laser scan data. Alignment approaches for robot mapping, like ICP or FFS, perform successfully only under the condition of sufficient feature overlap between single scans. This condition is often not met, e.g. in sparsely scanned environments or disaster areas for search and rescue robot tasks. Assuming mid level world knowledge (in the presented case the weak presence of noisy, roughly linear or rectangular-like objects) our system augments the sensor data with hypotheses ('Virtual Scans') about ideal models of these objects, based on analysis of a current estimated map of the underlying iterative alignment algorithm. Feedback between the data alignment and the data analysis confirms, modifies, or discards the Virtual Scan data in each iteration. Experiments with a simulated scenario and real world data from a rescue robot scenario show the applicability and advantages of the approach.

Augmenting Sparse Laser Scans with Virtual Scans to Improve the Performance of Alignment Algorithms