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Where did I take that snapshot? Scene-based homing by image matching

机译:我在哪里拍摄快照?通过图像匹配实现基于场景的归位

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In homing tasks, the goal is often not marked by visible objects but must be inferred from the spatial relation to the visual cues in the surrounding scene. The exact computation of the goal direction would require knowledge about the distances to visible landmarks, information, which is not directly available to passive vision systems. However, if prior assumptions about typical distance distributions are used, a snapshot taken at the goal suffices to compute the goal direction from the current view. We show that most existing approaches to scene-based homing implicitly assume an isotropic landmark distribution. As an alternative, we propose a homing scheme that uses parameterized displacement fields. These are obtained from an approximation that incorporates prior knowledge about perspective distortions of the visual environment. A mathematical analysis proves that both approximations do not prevent the schemes from approaching the goal with arbitrary accuracy, but lead to different errors in the computed goal direction. Mobile robot experiments are used to test the theoretical predictions and to demonstrate the practical feasibility of the new approach. [References: 38]
机译:在归位任务中,目标通常没有可见物体标记,而必须根据与周围场景中视觉线索的空间关系来推断。目标方向的准确计算将需要有关到可见地标的距离的知识,信息,而被动视觉系统无法直接获得这些信息。但是,如果使用关于典型距离分布的先前假设,则在目标处拍摄的快照足以从当前视图计算目标方向。我们表明,大多数现有的基于场景归位的方法都隐含了各向同性的地标分布。作为替代方案,我们提出了一种使用参数化位移场的归位方案。这些是从近似值获得的,该近似值包含有关视觉环境的透视失真的现有知识。数学分析证明,这两种近似方法均不能阻止方案以任意精度接近目标,但会导致计算出的目标方向出现不同的误差。移动机器人实验用于测试理论预测并证明新方法的实际可行性。 [参考:38]

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