论文标题
超越光度一致性：基于梯度的差异，用于改善视觉探光和立体声匹配
Beyond Photometric Consistency: Gradient-based Dissimilarity for Improving Visual Odometry and Stereo Matching
论文作者
论文摘要
姿势估计和地图构建是自主机器人的核心成分，通常依赖于传感器数据的注册。在本文中，我们研究了一个新的指标，用于注册基于光度误差的想法的图像。我们的方法结合了基于梯度方向的度量标准，并依赖于大小的缩放术语。我们将同时集成到立体声估计和视觉探测系统中，并在使用我们的指标时显示出明显的典型差异和直接图像注册任务的好处。我们的实验评估表明，我们的度量导致对场景深度和相机轨迹的更稳定，更准确的估计。因此，公制改善了相机姿势估计以及移动机器人的映射功能。我们认为，一系列现有的视觉探测器和视觉大满贯系统可以从本文报告的发现中受益。
Pose estimation and map building are central ingredients of autonomous robots and typically rely on the registration of sensor data. In this paper, we investigate a new metric for registering images that builds upon on the idea of the photometric error. Our approach combines a gradient orientation-based metric with a magnitude-dependent scaling term. We integrate both into stereo estimation as well as visual odometry systems and show clear benefits for typical disparity and direct image registration tasks when using our proposed metric. Our experimental evaluation indicats that our metric leads to more robust and more accurate estimates of the scene depth as well as camera trajectory. Thus, the metric improves camera pose estimation and in turn the mapping capabilities of mobile robots. We believe that a series of existing visual odometry and visual SLAM systems can benefit from the findings reported in this paper.
