KISS-IMU：基于运动平衡学习与不确定性感知推理的自监督惯性里程计
KISS-IMU: Self-supervised Inertial Odometry with Motion-balanced Learning and Uncertainty-aware Inference
AI总结：
提出KISS-IMU自监督框架，通过运动平衡训练和不确定性加权推理消除对真值依赖，仅用LiDAR作为轻量监督信号，实现鲁棒惯性里程计。
摘要：
惯性测量单元（IMU）提供高频线性加速度和角速度测量，是机器人系统中的基本传感模态。深度神经网络的最新进展在惯性里程计方面取得了显著进展。然而，训练过程中对真值数据的严重依赖从根本上限制了可扩展性以及对未见和多样化环境的泛化能力。我们提出KISS-IMU，一种新颖的自监督惯性里程计框架，通过利用简单的基于LiDAR的ICP配准和位姿图优化作为监督信号，消除了对真值的依赖。我们的方法体现了两个关键原则：通过运动感知平衡训练保持IMU稳定，以及通过推理过程中不确定性驱动的自适应加权保持IMU强大。为了评估不同运动模式和场景下的性能，我们在各种真实世界平台上进行了全面实验，包括四足机器人。重要的是，我们仅以自监督方式训练IMU网络，LiDAR仅作为轻量监督信号，而不需要额外的可学习过程。这种设计使框架能够在不依赖联合多模态学习或真值监督的情况下确保鲁棒性。补充材料可在此https URL获取。
英文摘要：
Inertial measurement units (IMUs), which provide high-frequency linear acceleration and angular velocity measurements, serve as fundamental sensing modalities in robotic systems. Recent advances in deep neural networks have led to remarkable progress in inertial odometry. However, the heavy reliance on ground truth data during training fundamentally limits scalability and generalization to unseen and diverse environments. We propose KISS-IMU, a novel self-supervised inertial odometry framework that eliminates ground truth dependency by leveraging simple LiDAR-based ICP registration and pose graph optimization as a supervisory signal. Our approach embodies two key principles: keeping the IMU stable through motion-aware balanced training and keeping the IMU strong through uncertainty-driven adaptive weighting during inference. To evaluate performance across diverse motion patterns and scenarios, we conducted comprehensive experiments on various real-world platforms, including quadruped robots. Importantly, we train only the IMU network in a self-supervised manner, with LiDAR serving solely as a lightweight supervisory signal rather than requiring additional learnable processes. This design enables the framework to ensure robustness without relying on joint multi-modal learning or ground truth supervision. The supplementary materials are available at https://sparolab.github.io/research/kiss_imu.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
