融合多传感器数据，100Hz输出更准的定位结果，有效抑制漂移。
FusionCore: A 23-State Unscented Kalman Filter for IMU, Wheel Encoder, GPS, and Visual SLAM Fusion in ROS 2
- 23状态无迹卡尔曼滤波，实时融合IMU、轮速计、GPS与视觉里程计
- 在12组数据上10次优于基准，轨迹误差降低1.2至22.2倍
- 支持无GPS环境运行，自动恢复地图重初始化，适合机器人定位开发
我们提出FusionCore，一个开源的ROS 2传感器融合包，使用23状态无迹卡尔曼滤波（UKF）将IMU、轮速计里程计、GPS和视觉SLAM位姿融合为单一100 Hz的里程计流。第23个状态是在线估计的轮速计系统性航向角偏差，通过GPS航向交叉协方差识别，并在GPS失联时扣除以减少巡航模式下的航向漂移。FusionCore还将陀螺仪和加速度计偏置作为显式滤波状态估计，原生在地心地固坐标系（ECEF）处理GPS数据，无需单独坐标投影节点；对各传感器采用基于马氏距离的卡方异常值剔除，校准至测量自由度；并根据创新序列自动调整传感器噪声协方差。视觉里程计/SLAM位姿融合使系统可在无GPS环境下运行，兼容任意视觉导航或SLAM系统，并能自动恢复地图重初始化。我们在NCLT公开数据集的十二个完整序列（每段55-92分钟）上进行评估，FusionCore在十组序列上取得更低的绝对轨迹误差（ATE），最优序列提升达1.2至22.2倍；而robot_localization UKF在全部十二组序列上出现数值发散。FusionCore项目地址：https://github.com/manankharwar/fusioncore，采用Apache 2.0许可。
原文摘要 · Abstract (English)
We present FusionCore, an open-source ROS 2 sensor fusion package that fuses IMU, wheel encoder odometry, GPS, and Visual SLAM pose into a single 100 Hz odometry stream using a 23-state Unscented Kalman Filter (UKF). The 23rd state is an online estimate of the wheel encoder's systematic yaw rate bias, identified through GPS heading cross-covariance and subtracted during GPS blackouts to reduce heading drift in coast mode. FusionCore also estimates gyroscope and accelerometer biases as explicit filter states, handles GPS natively in ECEF without a separate coordinate projection node, applies per-sensor Mahalanobis chi-squared outlier gating calibrated to measurement degrees of freedom, and adapts sensor noise covariance automatically from the innovation sequence. VSLAM pose fusion enables GPS-denied operation with any visual odometry or SLAM system, including automatic recovery from map reinitialization. We evaluate against robot_localization on twelve full-length sequences (55-92 min each) from the NCLT public dataset. FusionCore achieves lower Absolute Trajectory Error (ATE) on ten of twelve sequences, with improvements ranging from 1.2x to 22.2x on winning sequences. The robot_localization UKF diverges numerically on all twelve sequences. FusionCore is available at https://github.com/manankharwar/fusioncore under the Apache 2.0 license.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。