Scan alignment with probabilistic distance metric

Abstract

Scan alignment estimates the relative robot position from corresponding sets of data by identifying the transformation that minimizes a distance metric on these sets. Here, we present a method (SLIP) establishing correspondences between points based on a novel probabilistic distance metric to allow robust detection of outliers. This metric takes into account sensor noise and robot position uncertainty. Outliers are detected as elements with none but low probability links among all correspondences. To achieve scan alignment an inverse model is applied on the links, estimating robot position and reducing position uncertainty. Results of SLIP preserving all links and a computationally more efficient variant retaining only the most probable link are compared to standard ICP for tests with scan data and artificially inserted outliers. Additionally SLIP was used to built maps of an office environment from scan series. It was found to correct position errors and reject outliers from artificial data and real scans successfully.