发表机构
Technical University of Munich (TUM); Munich Institute of Robotics and Machine Intelligence (MIRMI); ETH Zurich（慕尼黑工业大学; 慕尼黑机器人与机器智能研究所; 苏黎世联邦理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对腿式机器人在非结构化环境中状态估计漂移问题，提出RAGNAROK雷达-视觉-运动学-惯性SLAM，整合多感知模块与校准技术，实验显示其性能优于现有方法。
AI 中文摘要
腿式机器人在非结构化环境中具备卓越的移动能力，但在此类条件下可靠运行需要鲁棒的状态估计。为解决 proprioceptive（本体感知）估计器在崎岖地形中的脆弱性问题，近期方法已引入雷达以提供速度测量。然而，这些方法的偏航可观测性有限，仍会导致漂移，且针对恶劣环境的故障感知融合研究仍不充分。在本文中，我们提出RAGNAROK，这是首个专为在挑战性环境中鲁棒运行设计的雷达-视觉-运动学-惯性SLAM。它整合了感知滑动和滚动接触的腿部速度估计、运动学感知的雷达因子以及退化感知的图像增强。我们进一步纳入基于B样条的雷达辅助本体感知主干、自适应加权和在线外参校准。在公开及自行采集的数据集上开展的大量实验表明，RAGNAROK在挑战性条件下实现了鲁棒性能，且优于当前最优基线。源代码和数据集可在该https URL获取。
英文摘要
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.
