FD-SLAM: 3-D Reconstruction Using Features and Dense Matching
IEEE International Conference on Robotics and Automation(2022)
Abstract
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.
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Key words
FD-SLAM,3-D reconstruction,visual SLAM systems,dense matching,long-term drift,map corruption,matching methods,long-term consistency,inaccurate local pose estimation,feature information,RGB-D SLAM system,frame-to-model odometry,accurate sub-maps,on-the-fly feature-based,global map optimisation,learning-based loop closure component,map building,map reconstruction quality
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