Direction-Aware Semi-Dense SLAM
This work addresses SLAM for robotics or autonomous systems by integrating scene understanding, though it appears incremental as it builds on existing probabilistic geometric methods.
The paper tackles the problem of simultaneous localization and mapping (SLAM) by developing the first direction-aware semi-dense SLAM system that jointly infers directional segmentation and a surfel-based map in real-time, resulting in improved SLAM accuracy and tracking efficiency at state-of-the-art performance.
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.