Probabilistic multi-level maps from LIDAR data

Recent research has shown that robots can model their world with Multi-Level (ML) maps, which utilize patches in a two-dimensional grid space to represent various environment elevations within a given grid cell. Although these maps are able to produce three-dimensional models of the environment while exploiting the computational feasibility of single elevation maps, they do not take into account in-plane uncertainty when matching measurements to grid cells or when grouping those measurements into patches. To respond to these drawbacks, this paper proposes to extend these ML maps into Probabilistic Multi-Level (PML) maps, which use formal probability theory to incorporate estimation and modeling errors due to uncertainty. Measurements are probabilistically associated with cells near the nominal location, and are categorized through hypothesis testing into patches via classification methods that incorporate uncertainty. Experimental results on representative objects found in both indoor and outdoor environments show that PML generally outperforms ML, including in noisy and sparse data environments, by producing more consistent, informative and conservative maps. In addition, PML provides the framework to heterogeneous, cooperative mapping and a way to probabilistically discriminate between conflicting maps.