From 533b1e8ea7981f19ccebe3f630bf022ab67eb03c Mon Sep 17 00:00:00 2001 From: Nemanja Djuric Date: Tue, 16 May 2023 23:49:47 +0000 Subject: [PATCH 1/2] Add Aurora MSDS to AWS Open Data --- datasets/aurora_msds.yaml | 49 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 49 insertions(+) create mode 100644 datasets/aurora_msds.yaml diff --git a/datasets/aurora_msds.yaml b/datasets/aurora_msds.yaml new file mode 100644 index 000000000..4dbecfc48 --- /dev/null +++ b/datasets/aurora_msds.yaml @@ -0,0 +1,49 @@ +Name: "Aurora Multi-Sensor Dataset" +Description: | + The Aurora Multi-Sensor Dataset is an open, large-scale multi-sensor dataset with highly accurate localization ground truth, captured between January 2017 and February 2018 in the metropolitan area of Pittsburgh, PA, USA by Aurora (via Uber ATG) in collaboration with the University of Toronto. The de-identified dataset contains rich metadata, such as weather and semantic segmentation, and spans all four seasons, rain, snow, overcast and sunny days, different times of day, and a variety of traffic conditions. +
+ The Aurora Multi-Sensor Dataset contains data from a 64-beam Velodyne HDL-64E LiDAR sensor and seven 1920x1200-pixel resolution cameras including a forward-facing stereo pair and five wide-angle lenses covering a 360-degree view around the vehicle. +
+ This data can be used to develop and evaluate large-scale long-term approaches to autonomous vehicle localization. Its size and diversity make it suitable for a wide range of research areas such as 3D reconstruction, virtual tourism, HD map construction, and map compression, among others. +
+ The data was first presented at the International Conference on Intelligent Robots and Systems (IROS) in 2020, where it was nominated as a Finalist for Best Application Paper at the conference. +Documentation: | + A third-party development kit authored by Andrei Bârsan of the University of Toronto, made available under the MIT License, can be found here: https://github.com/pit30m/pit30m. Aurora makes no representations as to the functionality or performance of the dev-kit. +Contact: ams-dataset@aurora.tech +ManagedBy: Aurora Operations, Inc. +UpdateFrequency: This dataset is complete. +Tags: + - aws-pds + - autonomous vehicles + - computer vision + - lidar + - mapping + - robotics + - transportation + - urban + - weather + - traffic + - localization + - simultaneous localization and mapping + - SLAM + - 3D reconstruction + - image processing + - machine learning + - deep learning +License: This data is intended for non-commercial academic use only. It is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. +Resources: + - Description: Aurora Multi-Sensor Dataset + ARN: arn:aws:s3:::pit30m + Region: us-east-1 + Type: S3 Bucket +DataAtWork: + Tutorials: + - Title: Introduction to Visualizing Sensor Types (Jupyter notebook) + URL: https://studiolab.sagemaker.aws/import/github/pit30m/pit30m/blob/main/examples/tutorial_00_introduction.ipynb + AuthorName: "Andrei Bârsan (note: Aurora makes no representations as to the accuracy or functionality of the tutorial)" + Services: + - SageMaker Studio Lab + Publications: + - Title: "\"Pit30M: A benchmark for global localization in the age of self-driving cars\", in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 4477-4484)" + URL: https://ras.papercept.net/images/temp/IROS/files/0132.pdf + AuthorName: Martinez, J., Doubov, S., Fan, J., Bârsan, I. A., Wang, S., Máttyus, G., Urtasun, R. From 5a5f20502c961c2ae32b38c0a9ec51d6653ffced Mon Sep 17 00:00:00 2001 From: Nemanja Djuric Date: Tue, 13 Jun 2023 22:21:47 +0000 Subject: [PATCH 2/2] Address reviewer comments --- datasets/aurora_msds.yaml | 4 ---- 1 file changed, 4 deletions(-) diff --git a/datasets/aurora_msds.yaml b/datasets/aurora_msds.yaml index 4dbecfc48..9ca9536ec 100644 --- a/datasets/aurora_msds.yaml +++ b/datasets/aurora_msds.yaml @@ -23,10 +23,6 @@ Tags: - urban - weather - traffic - - localization - - simultaneous localization and mapping - - SLAM - - 3D reconstruction - image processing - machine learning - deep learning