Point cloud descriptors for place recognition using sparse visual information
Point cloud descriptors for place recognition using sparse visual information
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DOI：
10.1109/icra.2016.7487687
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发表时间：
2016-05
期刊：
影响因子：
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通讯作者：
Titus Cieslewski;E. Stumm;A. Gawel;M. Bosse;Simon Lynen;R. Siegwart
中科院分区：
文献类型：
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作者：
Titus Cieslewski;E. Stumm;A. Gawel;M. Bosse;Simon Lynen;R. Siegwart
地点识别是同步定位和地图构建（SLAM）的核心组件，限制空间和时间上的位置漂移，以解锁精确的机器人导航。确定哪些以前访问过的地方属于一起仍然是一个高度活跃的研究领域，因为机器人应用需要越来越高的精度。已经提出了大量的位置识别算法，能够消耗各种传感器数据，包括激光，声纳和深度读数。然而，性能最好的解决方案已经通过匹配整个图像或其部分来利用视觉信息。最常见的是，基于视觉的方法的灵感来自信息检索，并利用3D几何信息的观察到的场景作为后验证步骤。在本文中，我们建议直接在位置识别管道的核心使用来自稀疏视觉特征图的3D场景信息。我们提出了一种新的结构描述子，它将SLAM中稀疏的三角形地标聚集成一个紧凑的签名。由此产生的3D特征提供了一个有区别的指纹，以识别季节和视点变化的地方，这对于基于稀疏视觉描述符的方法来说特别具有挑战性。我们在公开的数据集上评估了我们的系统，并展示了它的互补性如何改善视觉位置识别。
Place recognition is a core component in simultaneous localization and mapping (SLAM), limiting positional drift over space and time to unlock precise robot navigation. Determining which previously visited places belong together continues to be a highly active area of research as robotic applications demand increasingly higher accuracies. A large number of place recognition algorithms have been proposed, capable of consuming a variety of sensor data including laser, sonar and depth readings. The best performing solutions, however, have utilized visual information by either matching entire images or parts thereof. Most commonly, vision based approaches are inspired by information retrieval and utilize 3D-geometry information about the observed scene as a post-verification step. In this paper we propose to use the 3D-scene information from sparse-visual feature maps directly at the core of the place recognition pipeline. We propose a novel structural descriptor which aggregates sparse triangulated landmarks from SLAM into a compact signature. The resulting 3D-features provide a discriminative fingerprint to recognize places over seasonal and viewpoint changes which are particularly challenging for approaches based on sparse visual descriptors. We evaluate our system on publicly available datasets and show how its complementary nature can provide an improvement over visual place recognition.