Loosely-Coupled Semi-Direct Monocular SLAM
This addresses the problem of real-time localization and mapping for robotics or AR applications, with incremental improvements over existing methods.
The paper tackled monocular SLAM by proposing a loosely-coupled semi-direct method that combines direct and feature-based approaches, achieving state-of-the-art accuracy and robustness on benchmark datasets.
We propose a novel semi-direct approach for monocular simultaneous localization and mapping (SLAM) that combines the complementary strengths of direct and feature-based 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.