GSLAM: Initialization-Robust Monocular Visual SLAM via Global Structure-from-Motion
| Autor: | Chengzhou Tang, Ping Tan, Oliver Wang |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
FOS: Computer and information sciences
0209 industrial biotechnology Ground truth Monocular Computer science business.industry Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Initialization 02 engineering and technology Motion capture Matrix decomposition 020901 industrial engineering & automation Robustness (computer science) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence Visual odometry Global structure business |
| Zdroj: | 3DV |
| DOI: | 10.1109/3dv.2017.00027 |
| Popis: | Many monocular visual SLAM algorithms are derived from incremental structure-from-motion (SfM) methods. This work proposes a novel monocular SLAM method which integrates recent advances made in global SfM. In particular, we present two main contributions to visual SLAM. First, we solve the visual odometry problem by a novel rank-1 matrix factorization technique which is more robust to the errors in map initialization. Second, we adopt a recent global SfM method for the pose-graph optimization, which leads to a multi-stage linear formulation and enables L1 optimization for better robustness to false loops. The combination of these two approaches generates more robust reconstruction and is significantly faster (4X) than recent state-of-the-art SLAM systems. We also present a new dataset recorded with ground truth camera motion in a Vicon motion capture room, and compare our method to prior systems on it and established benchmark datasets. Comment: 3DV 2017 Project Page: https://frobelbest.github.io/gslam |
| Databáze: | OpenAIRE |
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