A Commute in Data: The comma2k19 Dataset

TL;DR

comma2k19 is a comprehensive dataset of over 33 hours of highway driving in California, including sensor data and accurate pose estimates, designed to facilitate development of GNSS and mapping algorithms.

Contribution

The paper introduces comma2k19, a new dataset with high-precision pose estimates and sensor data, along with Laika, an open-source GNSS processing library that improves positioning accuracy.

Findings

Laika produces 40% more accurate positions than raw GNSS data.

The dataset enables development of tightly coupled GNSS and mapping algorithms.

comma2k19 provides diverse sensor data suitable for autonomous driving research.

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.