Benchmarking urban six‐degree‐of‐freedom simultaneous localization and mapping

Quite a number of approaches for solving the simultaneous localization and mapping (SLAM) problem exist by now. Some of them have recently been extended to mapping environments with six‐degree‐of‐freedom poses, yielding 6D SLAM approaches. To demonstrate the capabilities of the respective algorithms, it is common practice to present generated maps and successful loop closings in large outdoor environments. Unfortunately, it is nontrivial to compare different 6D SLAM approaches objectively, because ground truth data about the outdoor environments used for demonstration are typically unavailable. We present a novel benchmarking method for generating the ground truth data based on reference maps. The method is then demonstrated by comparing the absolute performance of some previously existing 6D SLAM algorithms that build a large urban outdoor map.