CarlaScenes: A Synthetic Dataset for Odometry in Autonomous Driving

Andreas Kloukiniotis, Andreas Papandreou, Christos Anagnostopoulos, Aris Lalos, Petros Kapsalas, Duong-Van Nguyen, Konstantinos Moustakas; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2022, pp. 4520-4528

Despite the great scientific effort to capture adequately the complex environments in which autonomous vehicles (AVs) operate there are still uses-cases that even SoA methods fail to handle. Specifically in odometry problems, on the one hand, geometric solutions operate with certain assumptions that are often breached in AVs, and on the other hand, deep learning methods do not achieve high accuracy. To contribute to that we present CarlaScenes, a large-scale simulation dataset captured using the CARLA simulator. The dataset is oriented to address the challenging odometry scenarios that cause the current state of art odometers to deviate from their normal operations. Based on a case study of failures presented in experiments we distinguished 7 different sequences of data. CarlaScenes besides providing consistent reference poses, includes data with semantic annotation at the instance level for both image and lidar. The full dataset is available at https://github.com/CarlaScenes/CarlaSence.git.