InstaLoc：通过实例学习实现室内环境中的一次性全局激光雷达定位
InstaLoc: One-shot Global Lidar Localisation in Indoor Environments through Instance Learning
AI总结：
InstaLoc 提出一种基于全景分割和实例匹配的室内激光雷达一次性全局定位方法，通过两个高效网络实现实时定位，检测数量和精度均优于基线方法。
摘要：
自主机器人在先验地图中的定位对其功能至关重要。本文针对室内环境提出了一种名为 InstaLoc 的解决方案，该方案基于单帧激光雷达扫描，将其定位到先验地图中。我们借鉴了人类通过识别独特物体和结构的布局来进行导航和定位的方式。模仿人类的方法，InstaLoc 识别场景中的物体实例，并将其与先验地图中的实例进行匹配。据我们所知，这是第一种直接对 3D 激光雷达扫描进行全景分割推断以实现室内定位的方法。InstaLoc 通过两个基于空间稀疏张量的网络运行，直接推断密集的 3D 激光雷达点云。第一个网络是全景分割网络，生成物体实例及其语义类别。第二个较小的网络为每个物体实例生成一个描述子。然后，基于一致性的匹配算法将实例与先验地图进行匹配，并估计输入点云在先验地图中的六自由度（DoF）位姿。InstaLoc 的重要意义在于它拥有两个高效的网络。它只需在移动 GPU 上训练一到两个小时，并以 1 Hz 的频率实时运行。与基线方法相比，我们的方法在定位时获得的检测数量是基线的两到四倍，并且在这些检测上取得了更高的精度。
英文摘要：
Localization for autonomous robots in prior maps is crucial for their functionality. This paper offers a solution to this problem for indoor environments called InstaLoc, which operates on an individual lidar scan to localize it within a prior map. We draw on inspiration from how humans navigate and position themselves by recognizing the layout of distinctive objects and structures. Mimicking the human approach, InstaLoc identifies and matches object instances in the scene with those from a prior map. As far as we know, this is the first method to use panoptic segmentation directly inferring on 3D lidar scans for indoor localization. InstaLoc operates through two networks based on spatially sparse tensors to directly infer dense 3D lidar point clouds. The first network is a panoptic segmentation network that produces object instances and their semantic classes. The second smaller network produces a descriptor for each object instance. A consensus based matching algorithm then matches the instances to the prior map and estimates a six degrees of freedom (DoF) pose for the input cloud in the prior map. The significance of InstaLoc is that it has two efficient networks. It requires only one to two hours of training on a mobile GPU and runs in real-time at 1 Hz. Our method achieves between two and four times more detections when localizing, as compared to baseline methods, and achieves higher precision on these detections.
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。
