start-img


Overview

We present an ultra-wideband (UWB) time-difference-of-arrival (TDOA) dataset collected from a quadrotor platform for research purposes. The dataset consists of low-level signal information from static experiments (static dataset) and UWB TDOA measurements and additional onboard sensor data from flight experiments (flight dataset) in a variety of line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. The Loco Positioning System (LPS) from Bitcraze, based on DWM1000 UWB modules, is used to create this dataset. We hope this dataset can help researchers develop and compare reliable estimation methods for emerging UWB TDOA-based indoor localization technology.


Static Dataset

For the static experiments, we collected UWB TDOA measurements under various LOS and NLOS conditions. Two UWB anchors and one Crazyflie nano-quadrotor equipped with an UWB tag are placed on wooden structures. A millimeter-level accurate Vicon motion capture system measures the poses of the tag and the anchors for ground truth data.

Line-of-sight (LOS) experiments

In LOS conditions, we collected data from two tests: (i) the LOS distance test and (ii) the LOS angle test. The positions of the tag and anchor 2 are fixed throughout the LOS data collection process. In LOS distance test, we change the distance d1 from 0.5 meter to 6.5 meters with an interval of 0.5 meter. In LOS angle dataset, we change the angle from 180 degrees to 15 degrees with an interval of 15 degrees.

Non-line-of-sight (NLOS) experiments

During the NLOS tests, we fixed the positions of the tag and two anchors and placed different obstacles to block the line-of-sight of TDOA measurements. To reflect the comprehensive performance of UWB NLOS measurements, we selected six obstacles of different type of materials commonly used in indoor settings, including cardboard, metal, wood, plastic, and foam. We conducted NLOS experiments under (i) NLOS conditions between anchor 1 and the tag and (ii) NLOS conditions between anchor 1 and anchor 2. One LOS data is collected for comparison.

 

 

Static dataset format

In each sub-dataset, we provide a csv file containing the collected data and a txt file containing the poses of the tag and two anchors in one folder. The format of the csv file and brief descriptions of each value are summarized in table on the right. Detailed information can be found in the dataset paper.

 

 

 

 

 

 

 

 


Flight Dataset

For the flight experiments, we collected the raw UWB TDOA meaurements, gyroscope, accelerometer, optical flow, time-of-flight (ToF) laser-ranging, barometer, and the Vicon pose measurements (sent from the ground station) on-board a customized quadrotor platform.

Flight arena and experimental setup

The UWB TDOA flight dataset is produced in a 7.0 m × 8.0 m × 3.5 m indoor flight arena equipped with a motion capture system of 10 Vicon Vantage+ cameras. Printed Apriltags are attached to the soft mattresses to provide visual features for optical flow. For each sub-dataset, eight UWB anchors were pre-installed in the flight arena referred to as a constellation. Four different UWB constellations are used for data collection. The position and orientation of each anchor were surveyed using a mm-level accurate Leica total station for reproducibility.

We refer to the Vicon frame (see the right figure) as the inertial frame. To align the Leica total station frame and the inertial frame, we use the total station to survey six Vicon reflective markers with known positions in inertial frame and compute the transformation matrix through point cloud alignment. The average reprojection root-mean-squared error (RMSE) of the six reflective markers is around 1.12 mm.

Quadrotor platform

We built a customized quadrotor based on the Crazyflie Bolt flight controller with an inertial measurement unit (IMU) and attached commercially available extension boards (so-called decks) from Bitcraze for data collection. The LPS UWB tag is mounted vertically on the top to receive UWB TDOA measurements. A flow deck attached at the bottom provides optical flow measurements and a laser-based ToF sensor provides the local altitude information. The accelerometer and gyroscope data is obtained from the onboard IMU. A micro SD card deck logs the raw sensor data received by the flight control board with high-precision microsecond timestamps. The customized quadrotor communicates with a ground station computer over a 2.4 GHz USB radio dongle (Crazyradio PA) for high-level interaction. In terms of software, we use the Crazyswarm package to send high-level commands and the pose of the quadrotor measured by the motion capture system to the quadrotor.

Time synchronization, latency, and calibration

Onboard the quadrotor, the raw UWB measurements, gyroscope, accelerometer, optical flow, ToF laser-ranging, barometer, and the Vicon pose measurements (sent from the ground station) are recorded as event streams. The Vicon pose measurements logged onboard are treated as the ground truth data. Each datapoint is timestamped with the onboard microsecond timer and the resulting time series are written to the micro SD card as a binary file. Python scripts are provided to parse and analyze the binary data.

The latency from the ground station software to the onboard firmware is tested to be around 10 ms. As the length of each sub-dataset is around 120 seconds, we ignore the onboard clock drift. We refer to the offset between the center of a sensor and the vehicle center as sensor extrinsic parameters. The IMU is assumed to be aligned with the vehicle center. We provide the manually measured translation vectors from the center of the vehicle to onboard sensors (UWB tag and flow deck) in the dataset paper and the data parsing scripts.

Flight dataset format

In the flight dataset, we provide the UWB measurements under centralized TDOA mode (TDOA2) and decentralized TDOA mode (TDOA3). One centralized TDOA measurement and the Vicon ground truth are shown on the bottom image (left). We provide an error-state Kalman filter implementation for localization and the performance is demonstrated below (middle). Users are encouraged to design new algorithms to cope with the UWB measurement errors and noise for accurate indoor localizaiton.

To simulate more realistic and challenging conditions, we collected sensor data in a variety of cluttered environments with static and dynamic obstacles in constellation 4. One challenging NLOS condition induced by three wooden obstacle and one metal obstacle is demonstrated above (right). For the experiments with dynamic obstacles, we provide corresponding animations to visualize the experiment process. Two animations are shown below as examples.

For each UWB constellation, we provide the raw Leica total station survey results and computed anchor poses in txt files. In each sub-dataset, we provide the timestamped UWB TDOA, accelerometer, gyroscope, optical flow, ToF laser-ranger, and the barometer measurements and the ground truth measurements of the quadrotor’s pose in a csv file. The data format is shown in the following table. We also provide rosbag data converted from binary files for ROS related applications. We provide both Matlab and Python scripts to parse the data.

User Instructions

We provide the instructions for running the Python scripts. The data parsing scripts are developed and tested on an Ubuntu 20.04 laptop with ROS noetic installed. The corresponding Matlab scripts are developed on Matlab R2021a. Please change the path for the data (txt and csv files) on top of the Matlab scripts for usage.

Access data


Clone the Git repository and run the setupscript.bash file, which will download and decompose the dataset into the local git repository base folder.

$ git clone git@github.com:utiasDSL/util-uwb-dataset.git
$ cd util-uwb-dataset/
$ ./setupscript.bash 

You can also manually download the latest release of the dataset, and decompose the dataset into the local Git repository base folder.

ROS workspace


Step 1. Build ROS messages:

$ cd ros_ws/src
$ catkin_init_workspace
$ cd ..
$ catkin_make
$ source devel/setup.bash

NOTE: remember to source both your ROS environment and workspace.


Data parsing scripts for flight dataset

Step 2. Convert SD card binary data to rosbag:

$ cd scripts/flight-data/sdcard_scripts
$ python3 log_to_bag.py [SD_CARD_BINARY_DATA]                               
# e.g. python3 log_to_bag.py ../../../dataset/flight-dataset/binary-data/const1/const1-trial1-tdoa2

NOTE: we provide the converted rosbag data in the folder: “dataset/flight-dataset/rosbag-data/”.


Step 3. Convert the survey results to the inertial frame:

$ cd scripts/survey
$ python3 anchor_survey.py [SURVEY_RESULT_TXT]                              
# e.g. python3 anchor_survey.py ../../dataset/flight-dataset/survey-results/raw-data/anchor_const1.txt

NOTE: we provide the converted survey results (npz and txt files) in the folder: “dataset/flight-dataset/survey-results/”.


Step 4. Visualize UWB measurements:

$ cd scripts/flight-dataset
$ python3 visual_tdoa2_bag.py -i [ANCHOR_SURVEY_NPZ] [TDOA2_ROSBAG_DATA]        
# e.g. python3 visual_tdoa2_bag.py -i ../../dataset/flight-dataset/survey-results/anchor_const1.npz ../../dataset/flight-dataset/rosbag-data/const1/const1-trial1-tdoa2.bag 

$ python3 visual_tdoa3_bag.py -i [ANCHOR_SURVEY_NPZ] [TDOA3_ROSBAG_DATA]        
# e.g. python3 visual_tdoa3_bag.py -i ../../dataset/flight-dataset/survey-results/anchor_const1.npz ../../dataset/flight-dataset/rosbag-data/const1/const1-trial1-tdoa3.bag 

For TDOA3, the anchor pair of the visualized UWB measurement is set in the script visual_tdoa3_bag.py.


Step 5. Visualize UWB measurement bias:

$ cd scripts/flight-dataset
$ python3 visual_bias_bag.py -i [ANCHOR_SURVEY_NPZ] [TDOA_ROSBAG_DATA]          
# e.g. python3 visual_bias_bag.py -i ../../dataset/flight-dataset/survey-results/anchor_const1.npz ../../dataset/flight-dataset/rosbag-data/const1/const1-trial1-tdoa2.bag

The anchor pair of the visualized UWB measurement is set in the script visual_bias_bag.py


Step 6. Visualize the trajectory and static obstacle positions in constellation 3 and 4.

$ cd scripts/flight-dataset
$ python3 visual_obs_const3.py [ROSBAG_DATA]   
$ python3 visual_obs_const4.py [ROSBAG_DATA]
# e.g. python3 visual_obs_const3.py ../../dataset/flight-dataset/rosbag-data/const3/const3-trial8-tdoa2-manual1.bag 
# e.g. python3 visual_obs_const4.py ../../dataset/flight-dataset/rosbag-data/const4/const4-trial2-tdoa2-traj1.bag

Step 7. Error-State Kalman Filter Estimation

$ cd scripts/estimation
$ python3 main.py -i [ANCHOR_SURVEY_NPZ] [ROSBAG_DATA]                      
# e.g. python3 main.py -i ../../dataset/flight-dataset/survey-results/anchor_const1.npz ../../dataset/flight-dataset/rosbag-data/const1/const1-trial1-tdoa2.bag

Data parsing scripts for static dataset

Step 8. Visualize LOS static data

$ cd scripts/static-data
$ python3 los_visual.py [LOS_DATA_FOLDER]                                
# e.g. python3 los_visual.py ../../dataset/static-dataset/los/distTest/distT1

Step 9. Visualize NLOS static data

$ cd scripts/static-data
$ python3 nlos_visual.py [NLOS_DATA_FOLDER]                              
# e.g. python3 nlos_visual.py ../../dataset/static-dataset/nlos/anTag/metal/data1

Credits

This dataset was the work of Wenda Zhao, Abhishek Goudar, Xianyuan Qiao and Angela P. Schoellig. If you use the data provided by this website in your own work, please use the following citation:

@INPROCEEDINGS{zhao2022uwbData,
      title={UTIL: An Ultra-wideband Time-difference-of-arrival Indoor Localization Dataset}, 
      author={Wenda Zhao and Abhishek Goudar and Xianyuan Qiao and Angela P. Schoellig},
      booktitle={International Journal of Robotics Research (IJRR)},
      year={2022},
      volume={},
      number={},
      pages={},
      doi={}
}

start-img


University of Toronto’s Dynamic Systems Lab / Vector Institute/ UofT Robotics Institute