ASSESSING MAP QUALITY AND ERROR CAUSATION USING CONDITIONAL RANDOM FIELDS

摘要

This paper is about assessing the quality of maps built by a mobile robot. We extend previous work, which used solely geometric considerations, and use both temporal and spatial properties of the map to perform a binary classification of "plausible" and "suspicious". The use of the former allows the existence of low quality areas of the map to be attributed to missed loop closure events or local, online mapping errors. With an eye on our intended domain of urban operation, we adopt a Conditional Random Field as the probabilistic framework in which to model the spatial and temporal relationships between planar patches. The map quality labels are derived by using standard graph cuts optimization techniques. The approach is then illustrated with map created of an urban environment using data from a 3D laser range scanner mounted on a mobile robot.