论文标题
SD-DEFSLAM：半独立单眼大满贯，用于可变形和体内场景
SD-DefSLAM: Semi-Direct Monocular SLAM for Deformable and Intracorporeal Scenes
论文作者
论文摘要
传统的大满贯技术强烈依赖场景僵化来解决数据关联，忽略了场景的动态部分。在这项工作中，我们介绍了半导体Defslam（SD-DEFSLAM），这是一种新型的单眼可变形大量方法，能够映射在Defslam顶部建立的高度变形环境。为了在具有挑战性的变形场景中稳健地求解数据关联，SD-DEFSLAM结合了直接和间接方法：增强的照明不变性的数据关联卢卡斯 - 卡纳德跟踪器，基于摄像机重新安装的功能描述符的姿势和可变形地图估算的几何捆绑包调整，以及可变形的地图估计以及可变形的地图估算。使用对特定应用程序域进行训练的CNN检测并分割动态对象。我们在两个公共数据集中彻底评估我们的系统。曼陀罗数据集是一个巨大的基准测试，具有越来越侵略性的变形。 Hamlyn数据集包含体内序列，这些序列构成了严重的现实生活挑战，超出了变形，例如弱质地，镜面反射，手术工具和遮挡。我们的结果表明，由于所有数据关联步骤的改进，SD-DEFSLAM在点跟踪，重建精度和规模漂移中的表现都优于DEFSLAM，这是第一个能够在人体内部执行强劲猛击的系统。
Conventional SLAM techniques strongly rely on scene rigidity to solve data association, ignoring dynamic parts of the scene. In this work we present Semi-Direct DefSLAM (SD-DefSLAM), a novel monocular deformable SLAM method able to map highly deforming environments, built on top of DefSLAM. To robustly solve data association in challenging deforming scenes, SD-DefSLAM combines direct and indirect methods: an enhanced illumination-invariant Lucas-Kanade tracker for data association, geometric Bundle Adjustment for pose and deformable map estimation, and bag-of-words based on feature descriptors for camera relocation. Dynamic objects are detected and segmented-out using a CNN trained for the specific application domain. We thoroughly evaluate our system in two public datasets. The mandala dataset is a SLAM benchmark with increasingly aggressive deformations. The Hamlyn dataset contains intracorporeal sequences that pose serious real-life challenges beyond deformation like weak texture, specular reflections, surgical tools and occlusions. Our results show that SD-DefSLAM outperforms DefSLAM in point tracking, reconstruction accuracy and scale drift thanks to the improvement in all the data association steps, being the first system able to robustly perform SLAM inside the human body.
