H-RINS: スムージングとマッピングによる階層的密結合レーダー慣性状態推定
H-RINS: Hierarchical Tightly-coupled Radar-Inertial State Estimation via Smoothing and Mapping
レーダーとIMUを階層的な因子グラフで密結合し、高速なオドメトリ推定とループ閉じ込めによるバイアス補正を組み合わせてドリフトを抑える手法を提案した。
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著者: Ali Alridha Abdulkarim, Mikhail Litvinov, Dzmitry Tsetserukou
分類: cs.RO
原文アブストラクト
Millimeter-wave radar enables robust perception in visually degraded environments, yet radar-inertial estimation remains prone to drift: sparse body-frame velocity measurements weakly constrain absolute orientation, leaving IMU biases poorly observable over the short horizons of sliding-window estimators. We propose a tightly coupled, hierarchical radar-inertial factor graph that decouples estimation into a high-rate resetting graph and a persistent global graph. The resetting graph fuses IMU preintegration, radar velocities, and adaptive ZUPT to produce smooth, low-latency odometry for real-time control. The persistent graph maintains a full state (poses, velocities, and biases) via keyframe-based geometric mapping and loop closures. Fully observable biases and their exact covariances are continuously injected from the persistent graph as priors into the resetting graph, anchoring the high-rate estimator against integration drift. Extensive evaluations demonstrate high accuracy and drift-reduced estimation at faster than real-time speeds. Code and datasets will be released upon paper acceptance.
