LoopSmart: Smart Visual SLAM Through Surface Loop Closure
This work addresses the challenge of accurate localization and mapping in robotics and computer vision, representing an incremental improvement over existing SLAM techniques.
The paper tackles the problem of visual SLAM by introducing a framework that combines sparse feature matching and dense surface alignment to close surface loops, resulting in improved camera trajectory and surface reconstruction accuracy compared to state-of-the-art methods.
We present a visual simultaneous localization and mapping (SLAM) framework of closing surface loops. It combines both sparse feature matching and dense surface alignment. Sparse feature matching is used for visual odometry and globally camera pose fine-tuning when dense loops are detected, while dense surface alignment is the way of closing large loops and solving surface mismatching problem. To achieve smart dense surface loop closure, a highly efficient CUDA-based global point cloud registration method and a map content dependent loop verification method are proposed. We run extensive experiments on different datasets, our method outperforms state-of-the-art ones in terms of both camera trajectory and surface reconstruction accuracy.