Dense and accurate 3-D mapping from a monocular sequence is a key technology for several applications and still an open research area. This letter leverages recent results on single-view convolutional network (CNN)-based depth estimation and fuses them with multiview depth estimation. Both approaches present complementary strengths. Multiview depth is highly accurate but only in high-texture areas and high-parallax cases. Single-view depth captures the local structure of midlevel regions, including texture-less areas, but the estimated depth lacks global coherence. The single and multiview fusion we propose is challenging in several aspects. First, both depths are related by a deformation that depends on the image content. Second, the selection of multiview points of high accuracy might be difficult for low-parallax configurations. We present contributions for both problems. Our results in the public datasets of NYUv2 and TUM shows that our algorithm outperforms the individual single and multiview approaches. A video showing the key aspects of mapping in our single and multiview depth proposal is available at https: youtu.be ipc5HukTb4k.
Due to the difficulty of estimating an accurate and fully dense map from monocular views there are several approaches that estimate only the depth for the highest-gradient pixels (e.g., @cite_21 ). While this approach produces maps of higher density than the more traditional feature-based ones (e.g., @cite_5 ), they are still incomplete models of the scene and hence their applicability might be more limited.
@cite_5 @cite_21
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This paper presents ORB-SLAM, a feature-based monocular simultaneous localization and mapping (SLAM) system that operates in real time, in small and large indoor and outdoor environments. The system is robust to severe motion clutter, allows wide baseline loop closing and relocalization, and includes full automatic initialization. Building on excellent algorithms of recent years, we designed from scratch a novel system that uses the same features for all SLAM tasks: tracking, mapping, relocalization, and loop closing. A survival of the fittest strategy that selects the points and keyframes of the reconstruction leads to excellent robustness and generates a compact and trackable map that only grows if the scene content changes, allowing lifelong operation. We present an exhaustive evaluation in 27 sequences from the most popular datasets. ORB-SLAM achieves unprecedented performance with respect to other state-of-the-art monocular SLAM approaches. For the benefit of the community, we make the source code public. We propose a direct (feature-less) monocular SLAM algorithm which, in contrast to current state-of-the-art regarding direct methods, allows to build large-scale, consistent maps of the environment. Along with highly accurate pose estimation based on direct image alignment, the 3D environment is reconstructed in real-time as pose-graph of keyframes with associated semi-dense depth maps. These are obtained by filtering over a large number of pixelwise small-baseline stereo comparisons. The explicitly scale-drift aware formulation allows the approach to operate on challenging sequences including large variations in scene scale. Major enablers are two key novelties: (1) a novel direct tracking method which operates on ( sim (3) ), thereby explicitly detecting scale-drift, and (2) an elegant probabilistic solution to include the effect of noisy depth values into tracking. The resulting direct monocular SLAM system runs in real-time on a CPU.