SD-DefSLAM：半直接单目SLAM，用于可变形和颅内场景
SD-DefSLAM: Semi-Direct Monocular SLAM for Deformable and Intracorporeal Scenes
摘要：
传统的SLAM技术严重依赖场景刚性来解决数据关联，忽略了场景的动态部分。在这项工作中，我们提出了半直接DefSLAM（SD-DefSLAM），一种新的单目可变形SLAM方法，能够映射高度变形的环境，建立在DefSLAM之上。为了在具有挑战性的变形场景中稳健地解决数据关联问题，SD-DefSLAM结合了直接和间接方法：用于数据关联的增强照明不变Lucas-Kanade跟踪器，用于姿态和可变形地图估计的几何束调整，以及基于特征描述符的词袋用于相机重新定位。使用针对特定应用领域训练的CNN来检测和分割动态对象。我们在两个公共数据集中彻底评估了我们的系统。mandala数据集是一个SLAM基准，具有越来越积极的变形。Hamlyn数据集包含的内部序列除了变形之外，还带来了严重的现实挑战，如弱纹理、镜面反射、手术工具和遮挡。我们的研究结果表明，由于所有数据关联步骤的改进，SD-DefSLAM在点跟踪，重建精度和尺度漂移方面优于DefSLAM，是第一个能够在人体内稳健执行SLAM的系统。
英文摘要：
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.
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