A Non-rigid Approach to Scan Alignment and Change Detection Using Range Sensor Data

We present a probabilistic technique for alignment and subsequent change detection using range sensor data. The alignment method is derived from a novel, non-rigid approach to register point clouds induced by pose-related range observations that are particularly erroneous. It allows for high scan estimation errors to be compensated distinctly, whilst considering temporally successive measurements to be correlated. Based on the alignment, changes between data sets are detected using a probabilistic approach that is capable of differentiating between likely and unlikely changes. When applied to observations containing even small differences, it reliably identifies intentionally introduced modifications.