Constant-time wide-area monocular SLAM using absolute depth hinting

理解视觉环境是让机器与我们以及我们所占据的空间进行交互的关键，无论机器是采取完全自主的行动还是只是为我们提供额外的信息和建议。一个核心竞争力是能够重建移动相机周围环境的3D表示，并随时定位相机相对于它们的位置。在过去的几年中，在这个问题的视觉同步定位和映射（视觉SLAM），已经取得了巨大的实际进展，到现在，强大的现场重建是可能的适度的硬件上使用立体摄像机，更具有挑战性的是，只使用一个单一的camera.We关注在这里与单摄像机视觉SLAM，特别重要的是当有效载荷和功率清单必须保持小。现有技术允许包含从几百个视点成像的数万个3D点位置的重建在运行中被优化。虽然这听起来很大，但实际上这些数字将操作范围限制在中等规模的环境中。我们提出这项建议的目的是解决扩大这一范围的两个主要障碍。首先是算法的计算复杂度。随着环境的扩大，地图点的数量和/或相机位置的数量的多项式复杂性逐渐扼杀了实时操作。这意味着处理器速度的提高并不会导致地图大小的成比例增加，我们不能仅仅等待cpu的发展赶上。在这里，我们将继续我们最近在单目SLAM中的恒定时间探索方面的工作，其中只有相机周围的局部区域需要逐帧重新优化。第二是单目视觉固有的深度/速度缩放模糊性。单独使用图像运动，仅相对深度而不是绝对深度是可观察的。当摄像机四处移动时，不确定性不仅在位置和方向上增加，而且在周围环境的尺度上也增加。这导致返回到以前访问过的位置时增加了难度：不仅周围环境看起来被平移和扭曲，而且它们的大小也出现错误。但是，人类的独眼观察者不会受到同样的影响：无论是否被剥夺了立体视觉，我们都使用其他视觉线索来维持我们的尺度感，这些线索来自物体、物体类别和低层次的图像特征。正是这些，这个项目计划收集，以提供有关绝对深度的部分信息，足以消除解决方案中的额外自由度。（ii）它在平均恒定时间内运行，与它所构造的地图的大小完全无关，以及（ii）无论向其提供何种质量的深度信息，其都表现得优雅。

Understanding the visual environment is key to allowing machines interact with us and the space we occupy, whether the machine is to take fully autonomous action or just provide us with extra information and advice. A core competence is the ability to reconstruct a 3D representation of a moving camera's surroundings and to locate the camera relative to them from moment to moment. Over the last years, enormous practical strides have been made in this problem of visual simultaneous localization and mapping (visual SLAM), to the point now where robust live reconstruction is possible on modest hardware using stereo cameras and, more challengingly, using just a single camera.We are concerned here with single camera visual SLAM, of particular importance when the payload and power manifest has to be kept small. The state of the art allows reconstructions containing several tens of thousands of 3D point locations imaged from a few hundred viewpoints to be optimized on the fly. Though this sounds large, in practice these numbers restrict the operational scope to modestly-sized environments. Our aim in this proposal is to tackle the two chief impediments to increasing that scope. First is algorithmic computational complexity. Polynomial complexity in the number of map points and/or the number of camera positions gradually stifles live operation as the environment expands. This means that gains in processor speed do not lead to proportional gains in map size, and we cannot merely wait for cpu development to catch up. Here we will pursue our recent work on constant-time exploration in monocular SLAM, in which only a local region around the camera needs to be re-optimized frame-by-frame.Second is monocular vision's inherent depth/speed scaling ambiguity. Using image motion alone only relative depth, rather than absolute depth, is observable. As the camera moves around, uncertainty builds up not only in position and orientation, but also in the scale of the surroundings. This leads to added difficulty when returning to a previously visited location: not only do the surroundings appear translated and twisted, but they also appear the wrong size. But a human one-eyed observer does not suffer in the same way: whether deprived of stereo vision or not, we use other visual clues to maintain our sense of scale, clues from objects, object classes, and from low-level image traits. It is these that this project plans to glean to provide partial information about absolute depth, sufficient to remove that extra degree of freedom in the solution.We aim to produce a SLAM algorithm (i) that functions at video frame-rate; (ii) that functions in on-average constant time quite independently of the size of the map it is constructing, and (ii) that behaves gracefully whatever quality of depth information is provided to it.