Globally Consistent Range Scan Alignment for Environment Mapping

A robot exploring an unknown environment may need to build a worldmodel from sensor measurements. In order to integrate all the framesof sensor data, it is essential to align the data properly. Anincremental approach has been typically used in the past, in whicheach local frame of data is aligned to a cumulative global model, andthen merged to the model. Because different parts of the model areupdated independently while there are errors in the registration,such an approach may result in an inconsistent model.In this paper, we study the problem of consistent registration ofmultiple frames of measurements (range scans), together with therelated issues of representation and manipulation of spatialuncertainties. Our approach is to maintain all the local frames ofdata as well as the relative spatial relationships between localframes. These spatial relationships are modeled as random variablesand are derived from matching pairwise scans or from odometry. Thenwe formulate a procedure based on the maximum likelihood criterion tooptimally combine all the spatial relations. Consistency is achievedby using all the spatial relations as constraints to solve for thedata frame poses simultaneously. Experiments with both simulated andreal data will be presented.