Dense Piecewise Planar RGB-D SLAM for Indoor Environments
This work addresses robust SLAM for indoor environments with limited features, though it is incremental as it extends prior single-view parsing to video sequences.
The paper tackles online dense mapping and SLAM in low-texture indoor environments by exploiting weak Manhattan constraints and temporal continuity, achieving competitive results on the TUM benchmark where other methods fail.
The paper exploits weak Manhattan constraints to parse the structure of indoor environments from RGB-D video sequences in an online setting. We extend the previous approach for single view parsing of indoor scenes to video sequences and formulate the problem of recovering the floor plan of the environment as an optimal labeling problem solved using dynamic programming. The temporal continuity is enforced in a recursive setting, where labeling from previous frames is used as a prior term in the objective function. In addition to recovery of piecewise planar weak Manhattan structure of the extended environment, the orthogonality constraints are also exploited by visual odometry and pose graph optimization. This yields reliable estimates in the presence of large motions and absence of distinctive features to track. We evaluate our method on several challenging indoors sequences demonstrating accurate SLAM and dense mapping of low texture environments. On existing TUM benchmark we achieve competitive results with the alternative approaches which fail in our environments.