Multi-Sensor Characterisation Dataset and GUM Uncertainty Analysis Pipeline for a Low-Cost Indoor Autonomous Vehicle (Camera, 2D LiDAR, MEMS Gyroscope, Wheel Encoder)

This dataset contains characterisation measurements and a fully reproducible GUM-compliant (JCGM 100:2008) uncertainty-analysis pipeline for a five-sensor, low-cost perception suite of an indoor autonomous vehicle: an RGB detection camera (W7 Pro with YOLO), a lane camera (Sony IMX219), a 2D laser-triangulation LiDAR (YDLiDAR X2, characterised over a 36-angle × 40-distance grid, 10–400 cm), a MEMS gyroscope (MPU6050, 18 reference angles), and a 600 PPR optical wheel encoder (10 reference distances). Distances are referenced to a ±1 mm standard and angles to a ±0.5° index. The workbook (Data_Sensor.xlsx) provides one sheet per sensor, including datasheet parameters, reference values, and measured readings. The Python pipeline (multisensor_pipeline.py) reproduces every result table in the results/ folder: per-sensor calibration models (linear pulse-to-distance encoder calibration; gyroscope bias analysis; distance-indexed lookup-table LiDAR correction), GUM uncertainty budgets combining Type A and Type B components with Welch–Satterthwaite effective degrees of freedom and t-based coverage factors for small samples, and a system-level propagation with dominance analysis for obstacle localisation and dead reckoning. Headline results: expanded uncertainties of 0.72 cm (encoder distance, k = 2.28), 1.35° (heading), and 0.12 cm (LUT-corrected LiDAR range); heading uncertainty contributes over 99.7% of propagated obstacle-position variance. Recorded values are single-session means; the LiDAR grid exhibits zero cross-angle variance, so all LiDAR analyses are distance-only. This dataset accompanies a manuscript currently under double-anonymised review; author details will be added upon acceptance.本数据集包含一套面向室内自主无人车的五传感器低成本感知套件的表征测量数据，以及一套完全可复现的、符合《测量不确定度表示指南（Guide to the Expression of Uncertainty in Measurement，简称GUM，JCGM 100:2008）》的不确定度分析流程。该感知套件包含以下传感器：搭载YOLO的W7 Pro型RGB检测相机、索尼IMX219型车道相机、YDLiDAR X2型2D激光三角测距激光雷达（Light Detection and Ranging，LiDAR，在36角度×40距离的网格（10~400cm）下完成表征测试）、MPU6050型MEMS陀螺仪（包含18个参考角度），以及600脉冲每转（Pulse Per Revolution，PPR）光学轮式编码器（包含10个参考距离）。距离测量以±1mm的标准为基准，角度测量以±0.5°的分度值为基准。 工作簿Data_Sensor.xlsx为每个传感器单独设置工作表，包含器件手册参数、参考值与实测读数。Python分析流程multisensor_pipeline.py可复现results/文件夹下的全部结果表格，内容涵盖：单传感器校准模型（编码器脉冲-距离线性校准、陀螺仪偏置分析、基于距离索引查找表的激光雷达校正）；结合A类评定不确定度（Type A）与B类评定不确定度（Type B）分量的GUM不确定度评定流程，针对小样本采用Welch–Satterthwaite有效自由度与基于t分布的覆盖因子；以及面向障碍物定位与航位推算（dead reckoning）的系统级误差传播与主导因素分析。 核心结果如下：扩展不确定度分别为0.72cm（编码器距离测量，覆盖因子k=2.28）、1.35°（航向角）与0.12cm（查找表校正后的激光雷达测距）；航向不确定度占传播后的障碍物位置方差的99.7%以上。 记录值为单次实验的均值；该激光雷达的角度交叉方差为0，因此所有激光雷达分析仅针对距离维度。本数据集配合一篇目前处于双匿名评审阶段的论文，作者信息将在论文录用后补充。