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

理解视觉环境是让机器与我们互动以及我们占据的空间的关键，无论机器是采取完全自主的行动，还是只是向我们提供额外的信息和建议。核心能力是重建移动摄像机周围环境的3D表示并随时定位相对于它们的摄像机的能力。在过去的几年里，视觉同步定位和测绘(视觉SLAM)问题已经取得了巨大的实际进展，现在可以在使用立体摄像机的普通硬件上进行健壮的实时重建，更具挑战性的是，只使用一个摄像机。我们这里关注的是单摄像机视觉SLAM，当有效载荷和功率必须保持较小时，尤其重要。最先进的技术允许包含从数百个视点成像的数万个3D点位置的重建在运行中被优化。尽管这听起来很大，但实际上，这些数字将运营范围限制在中等规模的环境中。我们在这项提议中的目的是解决扩大这一范围的两个主要障碍。首先是算法计算的复杂性。随着环境的扩展，地图点数和/或相机位置数的多项式复杂性逐渐抑制了实时操作。这意味着处理器速度的提高不会导致映射大小成比例的增加，我们不能仅仅等待CPU开发赶上。在这里，我们将继续我们最近关于单目SLAM恒定时间探索的工作，其中只需要逐帧地重新优化相机周围的局部区域。第二个问题是单目视觉固有的深度/速度缩放模糊。单独使用图像运动只能观察到相对深度，而不是绝对深度。随着相机的移动，不确定性不仅在位置和方向上增加，而且在环境的规模上也是如此。这导致返回之前访问过的位置时增加了难度：不仅周围环境看起来平移和扭曲，而且它们的大小也错误。但人类的独眼观察者不会有同样的痛苦：无论是否失去了立体视觉，我们都会使用其他视觉线索来维持我们的尺寸感，从物体、物体类别和低层次图像特征中获得线索。本项目计划收集这些信息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。我们的目标是产生一种SLAM算法(I)在视频帧速率下运行；(Ii)在平均恒定时间内运行，完全独立于它正在构建的地图的大小；以及(Ii)无论提供给它的深度信息的质量如何，它都表现得优雅。

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.