KILVO：面向人形机器人的具备鲁棒多模态自适应的运动学-惯性-激光雷达-视觉里程计
KILVO: Kinematic-Inertial-LiDAR-Visual Odometry with Robust Multimodal Adaptation for Humanoid Robots
- Harbin Institute of Technology（哈尔滨工业大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出面向人形机器人的KILVO里程计，基于ESIKF整合多传感器并设计多模态自适应与接触估计模块，实验显示其精度、效率及鲁棒性优于现有方法，代码与数据集已开源。
AI中文摘要：
本文提出了一种面向人形机器人的运动学-惯性-激光雷达-视觉里程计，命名为KILVO。该方法针对人形机器人的平台特性、需求及现实世界复杂性量身定制，充分利用人形机器人通常配备的关节编码器、IMU、激光雷达、相机等传感器，将其整合至异步-顺序混合误差状态迭代卡尔曼滤波器（ESIKF）中。具体而言，惯性数据用于预测，腿部运动学以高频率异步处理并提供本体感知约束，而外部感知则按顺序更新：首先配准激光雷达点以获取几何先验，随后通过光度误差更新视觉分量。此外，该框架经过精心设计，具备多模态自适应能力以抵御传感器故障；还开发了紧凑的接触估计模块，无需额外传感器即可与状态估计共享信息。在公共数据集及真实场景中，针对多个人形机器人、步态模式和场景开展的大量实验表明，KILVO在精度、效率和输出频率方面具备极强竞争力，且对传感器退化和故障具有强鲁棒性，相比现有最先进的融合方法更适用于人形机器人。我们的代码和数据集已在GitHub上发布。
英文摘要：
This article presents a kinematic-inertial-LiDAR-visual odometry for humanoid robots, called KILVO. Tailored to the platform features, requirements, and real-world complexity, it fully utilizes the sensors commonly equipped on humanoid robots, including joint encoders, IMU, LiDAR, and camera, within an asynchronous-sequential hybrid error-state iterated Kalman filter (ESIKF). Specifically, inertial data are used for prediction, leg kinematics are processed asynchronously at a high rate and provide proprioceptive constraints, while exteroception is updated sequentially, first by registering LiDAR points for geometric priors and then by updating the visual component via photometric errors. Moreover, the framework is elaborately designed with multimodal adaptation for resilience to sensor failures. A compact contact estimation module is also developed, sharing information with state estimation without additional sensors. Extensive experiments on public datasets and in the real world across multiple humanoid robots, gait patterns, and scenarios demonstrate that KILVO achieves highly competitive accuracy, efficiency, and output rates, with strong robustness against sensor degradation and failures, making it more suitable for humanoid robots than state-of-the-art fusion methods. Our code and datasets are released on GitHub.
