A dataset for benchmarking vision-based localization at intersections

In this report we present the work performed in order to build a dataset for benchmarking vision-based localization at intersections, i.e., a set of stereo video sequences taken from a road vehicle that is approaching an intersection, altogether with a reliable measure of the observer position. This report is meant to complement our paper "Vision-Based Localization at Intersections using Digital Maps" submitted to ICRA2019. It complements the paper because the paper uses the dataset, but it had no space for describing the work done to obtain it. Moreover, the dataset is of interest for all those tackling the task of online localization at intersections for road vehicles, e.g., for a quantitative comparison with the proposal in our submitted paper, and it is therefore appropriate to put the dataset description in a separate report. We considered all datasets from road vehicles that we could find as for the end of August 2018. After our evaluation, we kept only sub-sequences from the KITTI dataset. In the future we will increase the collection of sequences with data from our vehicle.

[1]  Domenico G. Sorrenti,et al.  Rawseeds : Building a Benchmarking Toolkit for Autonomous Robotics , 2014 .

[2]  Francisco Angel Moreno,et al.  The Málaga urban dataset: High-rate stereo and LiDAR in a realistic urban scenario , 2014, Int. J. Robotics Res..

[3]  Ruigang Yang,et al.  The ApolloScape Dataset for Autonomous Driving , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[4]  Heiko Hirschmüller,et al.  Stereo Processing by Semiglobal Matching and Mutual Information , 2008, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Paul Newman,et al.  1 year, 1000 km: The Oxford RobotCar dataset , 2017, Int. J. Robotics Res..

[6]  Andreas Geiger,et al.  Vision meets robotics: The KITTI dataset , 2013, Int. J. Robotics Res..

[7]  Augusto Luis Ballardini,et al.  An online probabilistic road intersection detector , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[8]  Sanja Fidler,et al.  HD Maps: Fine-Grained Road Segmentation by Parsing Ground and Aerial Images , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Martin Lauer,et al.  A generative model for 3D urban scene understanding from movable platforms , 2011, CVPR 2011.

[10]  Min Bai,et al.  TorontoCity: Seeing the World with a Million Eyes , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).

[11]  Sanja Fidler,et al.  Enhancing Road Maps by Parsing Aerial Images Around the World , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[12]  Raquel Urtasun,et al.  DeepRoadMapper: Extracting Road Topology from Aerial Images , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[13]  Martin Lauer,et al.  3D Traffic Scene Understanding From Movable Platforms , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.