足触覚情報を取り入れたオンライン学習型脚運動学によるLiDAR-IMU-脚の密結合オドメトリ
Tightly-Coupled LiDAR-IMU-Leg Odometry with Online Learned Leg Kinematics Incorporating Foot Tactile Information
脚の触覚情報をオンライン学習するニューラル運動学モデルをファクタグラフに統合し、特徴の乏しい砂浜や不整地でもロバストに自己位置推定する手法を提案した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Taku Okawara, Kenji Koide, Aoki Takanose, Shuji Oishi, Masashi Yokozuka, Kentaro Uno, Kazuya Yoshida
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
In this letter, we present tightly coupled LiDAR-IMU-leg odometry, which is robust to challenging conditions such as featureless environments and deformable terrains. We developed an online learning-based leg kinematics model named the neural leg kinematics model, which incorporates tactile information (foot reaction force) to implicitly express the nonlinear dynamics between robot feet and the ground. Online training of this model enhances its adaptability to weight load changes of a robot (e.g., assuming delivery or transportation tasks) and terrain conditions. According to the \textit{neural adaptive leg odometry factor} and online uncertainty estimation of the leg kinematics model-based motion predictions, we jointly solve online training of this kinematics model and odometry estimation on a unified factor graph to retain the consistency of both. The proposed method was verified through real experiments using a quadruped robot in two challenging situations: 1) a sandy beach, representing an extremely featureless area with a deformable terrain, and 2) a campus, including multiple featureless areas and terrain types of asphalt, gravel (deformable terrain), and grass. Experimental results showed that our odometry estimation incorporating the \textit{neural leg kinematics model} outperforms state-of-the-art works. Our project page is available for further details: https://takuokawara.github.io/RAL2025_project_page/
