Dense Piecewise Planar RGB-D SLAM for Indoor Environments

TL;DR

This paper presents a novel RGB-D SLAM method that leverages weak Manhattan constraints and recursive labeling to accurately map indoor environments with low texture, large motions, and structural regularities.

Contribution

It extends single view indoor scene parsing to video sequences, integrating weak Manhattan constraints with recursive labeling and pose optimization for improved SLAM.

Findings

Achieves accurate dense mapping in low texture indoor environments.

Performs reliably with large motions and minimal feature tracking.

Demonstrates competitive results on TUM benchmark.

Abstract

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.