GeoFlow-SLAM: A Robust Tightly-Coupled RGBD-Inertial Fusion SLAM for Dynamic Legged Robotics

• URL: http://arxiv.org/abs/2503.14247v1
• Date: Tue, 18 Mar 2025 13:35:49 GMT
• Title: GeoFlow-SLAM: A Robust Tightly-Coupled RGBD-Inertial Fusion SLAM for Dynamic Legged Robotics
• Authors: Tingyang Xiao, Xiaolin Zhou, Liu Liu, Wei Sui, Wei Feng, Jiaxiong Qiu, Xinjie Wang, Zhizhong Su,
• Abstract summary: GeoFlow-SLAM is a robust and effective Tightly-Coupled RGBD-inertial SLAM for legged robots operating in highly dynamic environments.<n>The proposed algorithms achieve state-of-the-art on collected legged robots and open-source datasets.
• Score: 12.041115472752594
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents GeoFlow-SLAM, a robust and effective Tightly-Coupled RGBD-inertial SLAM for legged robots operating in highly dynamic environments.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/NSN-Hello/GeoFlow-SLAM

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