RAGNAROK: レーダー支援重力正規化アライメントによる堅牢なオープンキーフレームベースのレーダー・視覚・運動・慣性SLAM
RAGNAROK: Radar-Aided Gravity-Normalized Alignment for Robust Open Keyframe-based Radar-Visual-Kinematic-Inertial SLAM
脚ロボットの不整地での状態推定を堅牢にするため、レーダー・視覚・運動・慣性を統合したSLAMを提案。滑りや転がり接触を考慮した脚速度推定や劣化対応画像強調などを組み合わせ、厳しい環境でも高精度に動作する。
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著者: Hanjun Kim, Chiyun Noh, Sangwoo Jung, Jaehyung Jung, Simon Boche, Cedric Le Gentil, Stefan Leutenegger, Ayoung Kim
分類: cs.RO
原文アブストラクト
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.