Improving sparse laser scan alignment with Virtual Scans

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 overlap of features between individual scans. This condition is often not met, for example in sparsely scanned environments or disaster areas for search and rescue robot tasks. Assuming mid level world knowledge (in the presented case, weak presence of noisy, roughly linear or rectangular-like objects) our system augments the sensor data with hypotheses (dasiaVirtual Scanspsila) about ideal models of these objects. These hypotheses are generated by analyzing the current aligned map estimated by the underlying iterative alignment algorithm. The augmented data is used to improve the alignment process. Feedback between the data alignment and the data analysis confirms, modifies, or discards the Virtual Scans 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.