Dataset collected in an indoor industrial environment using a mobile unit (manually pushed trolley) that resembles an industrial vehicle equipped with several sensors, namely, Wi-Fi, wheel encoder (displacement), and Inertial Measurement Unit (IMU). Sensors were connected to a Raspberry Pi (RPi 3B +), which collected the data from the sensors. Ground truth information was obtained with video camera pointed towards the floor, registering the times when the trolley passed by reference tags. List of sensors: 4x Wi-Fi interfaces: Edimax EW7811-Un 2x IMUs: Adafruit BNO055 1x Absolute Encoder: US Digital A2 (attached to a wheel with a diameter of 125 mm) This dataset includes: 1x Wi-Fi radio map that can be used for Wi-Fi fingerprinting. 6x Trajectories: including sensor data + ground truth. APs Information: list of APs in the building, including their position and transmission channel. Floor plan: image of the building's floor plan with obstacles and non-navigable areas. Python package provided for: parsing the dataset into a data structure (Pandas dataframes). performing statistical analysis on the data (number of samples, time difference between consecutive samples, etc.). computing Dead Reckoning trajectory from a provided initial position. computing Wi-Fi fingerprinting position estimates. determining positioning error in Dead Reckoning and Wi-Fi fingerprinting. generating plots including the floor plan of the building, dead reckoning trajectories, and CDFs. When using this dataset, please cite its data description paper: Silva , I.; Pendão, C.; Torres-Sospedra, J.; Moreira, A. Industrial Environment Multi-Sensor Dataset for Vehicle Indoor Tracking with Wi-Fi, Inertial and Odometry Data. Data 2023, 8, 157. https://doi.org/10.3390/data8100157