GeoFlow-SLAM融合视觉惯性与足式机器人运动信息，提升高速动态环境下的定位精度。
GeoFlow-SLAM: A Robust Tightly-Coupled RGBD-Inertial and Legged Odometry Fusion SLAM for Dynamic Legged Robotics
- 通过双流光流结合地图点与位姿，增强高速运动时的特征匹配
- 在无纹理场景中实现长时稳定定位，精度优于现有方法
- 适合足式机器人在复杂动态环境中的实时导航与建图
本文提出GeoFlow-SLAM，一种针对足式机器人高速高频运动的鲁棒紧耦合RGBD-惯性SLAM系统。通过融合几何一致性、足式里程计约束与双流光流（GeoFlow），解决高速运动下特征匹配失败、位姿初始化困难及无纹理场景中视觉特征匮乏三大挑战。在快速运动中，利用双流光流结合先验地图点与位姿显著提升特征匹配性能；提出融合IMU/足式里程计、帧间PnP与广义迭代最近点（GICP）的鲁棒位姿初始化方法；首次引入深度到地图与GICP几何约束的紧耦合优化框架，显著提升长时无纹理环境下的定位鲁棒性与精度。所提算法在自采足式机器人数据集及开源数据集上均达到当前最优水平。代码与数据集将公开于https://github.com/HorizonRobotics/GeoFlowSlam。
原文摘要 · Abstract (English)
This paper presents GeoFlow-SLAM, a robust and effective Tightly-Coupled RGBD-inertial SLAM for legged robotics undergoing aggressive and high-frequency motions.By integrating geometric consistency, legged odometry constraints, and dual-stream optical flow (GeoFlow), our method addresses three critical challenges:feature matching and pose initialization failures during fast locomotion and visual feature scarcity in texture-less scenes.Specifically, in rapid motion scenarios, feature matching is notably enhanced by leveraging dual-stream optical flow, which combines prior map points and poses. Additionally, we propose a robust pose initialization method for fast locomotion and IMU error in legged robots, integrating IMU/Legged odometry, inter-frame Perspective-n-Point (PnP), and Generalized Iterative Closest Point (GICP). Furthermore, a novel optimization framework that tightly couples depth-to-map and GICP geometric constraints is first introduced to improve the robustness and accuracy in long-duration, visually texture-less environments. The proposed algorithms achieve state-of-the-art (SOTA) on collected legged robots and open-source datasets. To further promote research and development, the open-source datasets and code will be made publicly available at https://github.com/HorizonRobotics/GeoFlowSlam
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。