Problem
Research questions and friction points this paper is trying to address.
Legged robots
State estimation
Radar-Visual-Kinematic-Inertial SLAM
Yaw drift
Failure-aware fusion
RAGNAROK: Radar-Aided Gravity-Normalized Alignment for Robust Open Keyframe-based Radar-Visual-Kinematic-Inertial SLAM
🤖 AI Summary
This study addresses state estimation drift in legged robots traversing complex terrain, caused by proprioceptive disturbances and insufficient radar yaw observability. To this end, it proposes the first SLAM system integrating radar, vision, kinematics, and inertial measurements. Methodologically, we introduce a pioneering radar-aided gravity normalization alignment framework alongside a slip/roll contact-aware legged velocity estimator with adaptive weighted fusion. Furthermore, a B-spline radar-aided proprioceptive backbone network is constructed, incorporating kinematics-aware radar factors, online extrinsic calibration, and degraded image enhancement. Extensive experiments across multiple datasets demonstrate that the proposed system achieves significantly superior robustness over existing state-of-the-art methods in harsh environments.
📝 Abstract
Legged robots offer superior mobility in unstructured environments, but reliable operation in such conditions requires robust state estimation. To address the vulnerability of proprioceptive estimators in rough terrain, recent methods have incorporated radar to provide velocity measurements. However, their limited yaw observability still leads to drift, and failure-aware fusion for adverse environments remains underexplored. In this letter, we present RAGNAROK, the first radar-visual-kinematic-inertial SLAM designed for robust operation in challenging environments. It integrates slip- and rolling-contact-aware leg velocity estimation, a kinematics-aware radar factor, and degradation-aware image enhancement. We further incorporate a B-spline-based radar-aided proprioceptive backbone, adaptive weighting, and online extrinsic calibration. Extensive experiments on public and self-collected datasets demonstrate that RAGNAROK achieves robust performance under challenging conditions and outperforms state-of-the-art baselines. The source code and dataset are available at https://github.com/hanjun815/RAGNAROK.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Radar-Visual-Inertial SLAM
Legged Robots
B-spline Optimization
Adaptive Fusion
Degradation-aware Enhancement
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