融合多传感器的足式机器人里程计，提升复杂环境下的定位精度与鲁棒性。
KILVO: Kinematic-Inertial-LiDAR-Visual Odometry with Robust Multimodal Adaptation for Humanoid Robots
- 异步-顺序混合滤波框架，分步处理惯性、关节、激光与视觉数据。
- 在多个真实场景中实现高精度定位，支持高速关节更新与低延迟输出。
- 具备抗传感器失效能力，适合足式机器人复杂运动需求。
本文提出一种面向足式机器人的运动学-惯性-激光雷达-视觉里程计（KILVO），充分利用足式机器人常见的关节编码器、IMU、LiDAR和相机等传感器。采用异步-顺序混合误差状态迭代卡尔曼滤波（ESIKF）框架：惯性数据用于预测，高频率异步处理腿部运动学提供本体感觉约束，外部感知则按序更新——先通过激光点云配准获得几何先验，再利用光度误差优化视觉组件。系统设计了多模态自适应机制以应对传感器故障，并集成轻量接触估计模块，与状态估计共享信息而无需额外传感器。在多个公开数据集及真实世界中，针对多种足式机器人、步态模式与场景进行实验，结果表明KILVO在精度、效率与输出速率方面均表现优异，对传感器退化和故障具有强鲁棒性，优于现有融合方法。代码与数据已开源。
原文摘要 · Abstract (English)
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.
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