用于宽视差重定位的对象增强型RGB-D SLAM
Object-Augmented RGB-D SLAM for Wide-Disparity Relocalisation
摘要：
我们提出了一种新的对象增强的RGB-D SLAM系统，该系统能够构建一致的对象地图，并根据地图中对象的质心进行重新定位。该方法旨在克服使用点特征或图像的基于外观的重新定位方法的视图依赖性。在地图构建过程中，我们使用预先训练的神经网络来检测物体并从RGB-D数据中估计6D姿态。增量概率模型用于随时间聚合估计以创建对象映射。然后在重新定位中，我们使用相同的网络来提取“丢失”帧中的感兴趣对象。成对几何匹配发现地图和帧对象之间的对应关系，以及概率绝对定向，然后将迭代最近点应用于密集深度图和对象质心，从而提供重新定位。在桌面环境中的实验结果表明，即使是具有广泛不同的观点，从那些用来构建地图，显着优于两个外观为基础的方法帧非常高的成功率。
英文摘要：
We propose a novel object-augmented RGB-D SLAM system that is capable of constructing a consistent object map and performing relocalisation based on centroids of objects in the map. The approach aims to overcome the view dependence of appearance-based relocalisation methods using point features or images. During the map construction, we use a pre-trained neural network to detect objects and estimate 6D poses from RGB-D data. An incremental probabilistic model is used to aggregate estimates over time to create the object map. Then in relocalisation, we use the same network to extract objects-of-interest in the `lost' frames. Pairwise geometric matching finds correspondences between map and frame objects, and probabilistic absolute orientation followed by application of iterative closest point to dense depth maps and object centroids gives relocalisation. Results of experiments in desktop environments demonstrate very high success rates even for frames with widely different viewpoints from those used to construct the map, significantly outperforming two appearance-based methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
