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comma2k19 (10 files)
Chunk_1.zip8.73GB
Chunk_10.zip9.90GB
Chunk_2.zip9.05GB
Chunk_3.zip9.41GB
Chunk_4.zip9.49GB
Chunk_5.zip9.81GB
Chunk_6.zip9.53GB
Chunk_7.zip9.29GB
Chunk_8.zip9.63GB
Chunk_9.zip9.77GB
Type: Dataset
Tags: Dataset, robotics, sensor fusion, GNSS, tightly coupled, mapping

Bibtex:
@article{,
title= {comma2k19},
keywords= {Dataset, robotics, sensor fusion, GNSS, tightly coupled, mapping},
journal= {},
author= {Harald Schafer and Eder Santana and Andrew Haden and Riccardo Biasini},
year= {},
url= {https://github.com/commaai/comma2k19},
license= {MIT License},
abstract= {comma.ai presents comma2k19, a dataset of over 33 hours of commute in
California's 280 highway. This means 2019 segments, 1 minute long each, on a
20km section of highway driving between California's San Jose and San
Francisco. The dataset was collected using comma EONs that have sensors similar
to those of any modern smartphone including a road-facing camera, phone GPS,
thermometers and a 9-axis IMU. Additionally, the EON captures raw GNSS
measurements and all CAN data sent by the car with a comma grey panda. Laika,
an open-source GNSS processing library, is also introduced here. Laika produces
40% more accurate positions than the GNSS module used to collect the raw data.
This dataset includes pose (position + orientation) estimates in a global
reference frame of the recording camera. These poses were computed with a
tightly coupled INS/GNSS/Vision optimizer that relies on data processed by
Laika. comma2k19 is ideal for development and validation of tightly coupled
GNSS algorithms and mapping algorithms that work with commodity sensors.},
superseded= {},
terms= {}
}