Estimating Motion From Sparse Range Data Without Correspondence
Estimating Motion From Sparse Range Data Without Correspondence
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从没有对应关系的稀疏范围数据估计运动
DOI：
10.1109/ccv.1988.589992
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发表时间：
1988
期刊：
影响因子：
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通讯作者：
R. Szeliski
中科院分区：
文献类型：
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作者：
R. Szeliski
从随时间变化的范围内估计观察者的运动并将这些数据融合到环境的连贯地图中是机器人导航中的两个重要问题。当前的方法首先确定从不同视点获取的距离测量之间的对应关系，然后根据该对应关系计算运动估计。在本文中，我们提出了一种替代技术，该技术不假设存在任何此类对应关系。 Instcad，使用光滑表面假设，即感测点位于某个分段光滑表面上。通过查找几何变换来获得运动估计，该几何变换使得这些点最有可能（在贝叶斯意义上）来自同一表面。我们推导出一个能量方程，该方程测量新数据点和正在增量重新绘制的密集插值深度图之间的距离。最佳运动估计附近的能量方程的形状用于计算估计中的不确定性。由此产生的模函数估计算法可以与其他运动激励系统结合使用，并提供一种灵活且鲁棒的方法来计算稀疏范围数据的运动。点集，以便将它们集成到更新的表面估计中。我们的方法基于光滑表面，即，使用测距仪（从两个或多个视点）感测到的点被假定位于分段光滑表面上：我们的算法找到使这些点集最有可能来自同一分段平滑场景的运动。实际上，似然度量的对数与新数据点和当前表面估计之间的距离的加权和密切相关。因此，该方法非常适合增量传感策略，其中通过集成移动相机的测量值获得密集的深度估计~IC（Matthies88）。本文提出的方法展示了如何测量特定变换点集合被正确“注册”的可能性，以及如何使用梯度下降找到局部最优运动估计。然而，本文并未解决如何在可能的变换的大空间中搜索“最佳”运动的问题。因此，我们的方法旨在与其他一些运动估计系统（例如惯性导航系统）结合使用，用于在解附近启动梯度下降算法。如果可能的运动范围很小，我们的方法也将起作用，这可以通过足够快地采样数据来确保（如实时机器人控制中的情况）。我们开发的运动估计算法可应用于移动机器人导航和机器人操纵。作为移动机器人系统的一部分，该算法用于细化或改进从其他来源（例如惯性导航、死点重算或地标识别）获得的运动估计。该算法还建立并维护环境的密集深度图，可用于与其他传感器集成。该地图可以是视网膜主题（基于图像）深度图或基于地形的高程图（Olin88）。我们的算法特别适合地形图，因为它可以处理规则间隔的数据点（从透视反投影），吸收制图数据的先验知识，并融合仅具有有限重叠的数据。在机器人操纵中，我们的算法可用于从稀疏的触觉数据中确定对象或观察者的运动。本文使用的一般方法是通过插值和积分稀疏范围数据来增量构建密集深度图，并将新点与该表面匹配以执行运动估计。因此，我们首先回顾一下
Estimating observer motion from time-varying range &ita and fusing this data into a coherent map of the environment are two impor- tant problems in robot navigation. Current methods first detenmine a correspondence between range measurements acquired from different viewpoints, and then compute a motion estimate from this correspon- dence. In this paper, we present an alternative technique which does not assume that any such correspondence exists. Instcad, a smooth surface assumption is used, i.e., the sensed points are imedl to lie on some piecewise smooth surface. A motion estimate is obtained by finding the geometric transformation which makes it most likely (in a Bayesian sense) that the points came from the same surface. We derive an energy equation which measures the distance bctween the new data pints and the dense interpolated depth map wllich is being incrementally rehed. The shape of the energy equation in the neighborhood of the optimal motion estimate is used to com- pute the uncertainty in the estimate. The resulting modon estimation algorithm can be used in conjunction with other motion stirnation systems, and provides a flexible and robust method for computing motion ffom sparse range data. sets of points so that they may be integrated into an updated surface estimate. Our metliad is based on a smooth surface usswnption, i.e., the points which are sensed with the range finder (from two or more viewpoints) are assumed to lie on a piecewise smooth surface:. Our algorithm finds the motion which makes it most likely that these sets of points are from the same piecewise smooth scene. In practice, the logarithm of the likelihood measure itums out to be closely related to the weighted sum of squan:s distance between the new data points and the current surface estimate. This method thus fits well with incremental sensing strategies, where dense depth estimates ~IC obtained by integrating measurements taken from a moving camera (Matthies88). The method presented in this paper shows how to mea- sure the the likelihood of a particular collection of transformed points being properly "regisxered", and how to find a locally optimal motion estimate using gradient descent. This paper does not, however, address tlhe issue of how to search the large space of possible transformations for the "best" motion. Our method is thus meant to b, used in conjunction with some other motion estimation system - such as an inertial navi- gation system - which is used to start the gradient descent algorithm in the vicinity of the solution. Our method will also work if the range of possible motions is small, which can be ensured by sampling the data sufficiently rapidly (as is the case in real-time robot control). The motion estimation algorithm we develop can be ap- plied to both mobile robot navigation and robot manipulation. As part of a mobile robot syslem, the algorithm is used to refine or improve motion estimates obtained from other sources such as inemal navigation, dead retkoning or landmark recognition. The algorithm also builds up and maintains a dense depth map of the environment which can, be used for integration with other sensors. This map can either be a retinotopic (image-based) depth map or a terrain-based elevation map (Olin88). Our al- gorithm is particularly well suited for terrain maps since it can handle data points that are irn:gularly spaced (from perspective de-projection), incaporate prior knowledge from cartographic data, and fuse data with only limited mas of overlap. In robot manipulation, our algorithm can be used to determine object or observer motion from sparse tactile data. The general approach uised in this paper is to incremen- tally build up a dense depth map by interpolating and integrat- ing sparse range data, and to match new points to this surface to perform the motion estimation. We thus start by reviewing