基于激光雷达机器人检测的退化感知协同多模态GNSS拒止定位
Degradation-Aware Cooperative Multi-Modal GNSS-Denied Localization Leveraging LiDAR-Based Robot Detections
AI总结：
提出一种因子图框架下的自适应多模态多机器人协同定位方法，融合异步VIO、LIO和3D机器人间检测，通过插值因子和Wasserstein距离加权处理传感器退化，显著提升定位精度。
摘要：
在无全球导航卫星系统（GNSS）环境中，使用机载传感器进行精确长期定位对机器人至关重要。虽然互补传感器可以缓解个体退化，但在单个机器人上携带所有可用传感器类型会显著增加尺寸、重量和功耗需求。将传感器分布在多个机器人上可增强部署能力，但引入了来自独立移动平台的异步多模态数据融合的挑战。我们提出了一种新颖的自适应多模态多机器人协同定位方法，采用因子图公式，以松耦合方式融合来自不同机器人的异步视觉-惯性里程计（VIO）、激光雷达-惯性里程计（LIO）和3D机器人间检测。该方法适应变化的条件，利用可靠数据帮助受传感器退化影响的机器人。一种新颖的基于插值的因子实现了非同步测量的融合。LIO退化基于近似扫描匹配Hessian进行评估。提出了一种根据连续VIO输出之间的Wasserstein距离按比例加权里程计数据的新方法。提供了理论分析，研究了各种条件下（主要是在传感器退化存在时）的协同定位问题。该方法已在使用无人地面车辆（UGV）和无人飞行器（UAV）异构团队收集的真实世界数据上进行了广泛评估，结果表明该方法在各种传感器退化情况下显著提高了定位精度。
英文摘要：
Accurate long-term localization using onboard sensors is crucial for robots operating in Global Navigation Satellite System (GNSS)-denied environments. While complementary sensors mitigate individual degradations, carrying all the available sensor types on a single robot significantly increases the size, weight, and power demands. Distributing sensors across multiple robots enhances the deployability but introduces challenges in fusing asynchronous, multi-modal data from independently moving platforms. We propose a novel adaptive multi-modal multi-robot cooperative localization approach using a factor-graph formulation to fuse asynchronous Visual-Inertial Odometry (VIO), LiDAR-Inertial Odometry (LIO), and 3D inter-robot detections from distinct robots in a loosely-coupled fashion. The approach adapts to changing conditions, leveraging reliable data to assist robots affected by sensory degradations. A novel interpolation-based factor enables fusion of the unsynchronized measurements. LIO degradations are evaluated based on the approximate scan-matching Hessian. A novel approach of weighting odometry data proportionally to the Wasserstein distance between the consecutive VIO outputs is proposed. A theoretical analysis is provided, investigating the cooperative localization problem under various conditions, mainly in the presence of sensory degradations. The proposed method has been extensively evaluated on real-world data gathered with heterogeneous teams of an Unmanned Ground Vehicle (UGV) and Unmanned Aerial Vehicles (UAVs), showing that the approach provides significant improvements in localization accuracy in the presence of various sensory degradations.
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