FD-SLAM: 3-D Reconstruction Using Features and Dense Matching
This addresses the problem of drift and map corruption in SLAM for robotics and AR/VR applications, offering an incremental improvement by hybridizing existing methods.
The paper tackles the trade-off between local accuracy and long-term consistency in visual SLAM by proposing an RGB-D system that combines dense matching for sub-maps with feature-based matching for global optimization, achieving performance on par or better than state-of-the-art in map reconstruction and pose estimation on indoor datasets.
It is well known that visual SLAM systems based on dense matching are locally accurate but are also susceptible to long-term drift and map corruption. In contrast, feature matching methods can achieve greater long-term consistency but can suffer from inaccurate local pose estimation when feature information is sparse. Based on these observations, we propose an RGB-D SLAM system that leverages the advantages of both approaches: using dense frame-to-model odometry to build accurate sub-maps and on-the-fly feature-based matching across sub-maps for global map optimisation. In addition, we incorporate a learning-based loop closure component based on 3-D features which further stabilises map building. We have evaluated the approach on indoor sequences from public datasets, and the results show that it performs on par or better than state-of-the-art systems in terms of map reconstruction quality and pose estimation. The approach can also scale to large scenes where other systems often fail.