Loosely-Coupled Semi-Direct Monocular SLAM

Seong Hun Lee, Javier Civera

Abstract:

We propose a novel semi-direct approach for
monocular simultaneous localization and mapping (SLAM) that
combines the complementary strengths of direct and featurebased methods. The proposed pipeline loosely couples direct
odometry and feature-based SLAM to perform three levels
of parallel optimizations: (1) photometric bundle adjustment
(BA) that jointly optimizes the local structure and motion,
(2) geometric BA that refines keyframe poses and associated
feature map points, and (3) pose graph optimization to achieve
global map consistency in the presence of loop closures. This is
achieved in real-time by limiting the feature-based operations
to marginalized keyframes from the direct odometry module.
Exhaustive evaluation on two benchmark datasets demonstrates
that our system outperforms the state-of-the-art monocular
odometry and SLAM systems in terms of overall accuracy and
robustness.