  To aide simultaneous localization and mapping (SLAM), future perception
systems will incorporate forms of scene understanding. In a step towards fully
integrated probabilistic geometric scene understanding, localization and
mapping we propose the first direction-aware semi-dense SLAM system. It jointly
infers the directional Stata Center World (SCW) segmentation and a surfel-based
semi-dense map while performing real-time camera tracking. The joint SCW map
model connects a scene-wide Bayesian nonparametric Dirichlet Process
von-Mises-Fisher mixture model (DP-vMF) prior on surfel orientations with the
local surfel locations via a conditional random field (CRF). Camera tracking
leverages the SCW segmentation to improve efficiency via guided observation
selection. Results demonstrate improved SLAM accuracy and tracking efficiency
at state of the art performance.
