用于无基站精准机器人定位的原始GNSS传感与IMU及激光雷达的因子图融合
Factor Graph Fusion of Raw GNSS Sensing with IMU and Lidar for Precise Robot Localization without a Base Station
- Univ. of Oxford（牛津大学）
- Oxford Robotics Inst.（牛津机器人研究所）
- Dept. of Eng. Science（工程科学系）
- Free Univ. of Bozen-Bolzano（博尔扎诺自由大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究提出一种基于因子图的多传感器紧融合方法，融合原始GNSS、IMU及可选激光雷达数据，无需基站即可实现机器人在不同环境下的高精度平滑全局定位。
AI中文摘要：
精准定位是机器人导航系统的核心组成部分。为此，全球导航卫星系统（GNSS）可在户外提供绝对测量值，从而消除长期漂移。然而，将GNSS数据与其他传感器数据融合并非易事，尤其是当机器人在有天空视野和无天空视野的区域之间移动时。我们提出了一种鲁棒的方法，将原始GNSS接收机数据与惯性测量值以及可选的激光雷达观测值进行紧融合，以实现精准且平滑的移动机器人定位。我们提出了包含两类GNSS因子的因子图：第一类是基于伪距的因子，可实现地球上的全局定位；第二类是基于载波相位的因子，可实现高精度相对定位，这在其他传感模式受限时十分有用。与传统差分GNSS不同，该方法无需连接基站。在一个公开的城市驾驶数据集上，我们的方法取得了与融合视觉惯性里程计和GNSS数据的SOTA算法相当的精度——尽管我们的方法未使用相机，仅使用了惯性和GNSS数据。我们还利用在森林等天空视野极少的环境中行驶的汽车和四足机器人的采集数据，验证了该方法的鲁棒性。其在全球地球坐标系下的精度仍可达1-2米，且估计的轨迹无间断、平滑。我们还展示了如何将激光雷达测量值进行紧集成。我们认为这是首个在因子图中融合原始GNSS观测值（而非定位解算值）与激光雷达的系统。
英文摘要：
Accurate localization is a core component of a robot's navigation system. To this end, global navigation satellite systems (GNSS) can provide absolute measurements outdoors and, therefore, eliminate long-term drift. However, fusing GNSS data with other sensor data is not trivial, especially when a robot moves between areas with and without sky view. We propose a robust approach that tightly fuses raw GNSS receiver data with inertial measurements and, optionally, lidar observations for precise and smooth mobile robot localization. A factor graph with two types of GNSS factors is proposed. First, factors based on pseudoranges, which allow for global localization on Earth. Second, factors based on carrier phases, which enable highly accurate relative localization, which is useful when other sensing modalities are challenged. Unlike traditional differential GNSS, this approach does not require a connection to a base station. On a public urban driving dataset, our approach achieves accuracy comparable to a state-of-the-art algorithm that fuses visual inertial odometry with GNSS data -- despite our approach not using the camera, just inertial and GNSS data. We also demonstrate the robustness of our approach using data from a car and a quadruped robot moving in environments with little sky visibility, such as a forest. The accuracy in the global Earth frame is still 1-2 m, while the estimated trajectories are discontinuity-free and smooth. We also show how lidar measurements can be tightly integrated. We believe this is the first system that fuses raw GNSS observations (as opposed to fixes) with lidar in a factor graph.
