Moderation Team
Workshop: Collaborative Perception & Federated ML for Autonomous Driving
Link : https://sites.google.com/view/cofed-dlad-2020/schedule
Speaker : Dr. Renaud Dubé
Abstract : Precisely recognizing places is a fundamental capability for collaborative mapping and re-localization in multi-agents systems. This task, however, remains challenging in unstructured, dynamic environments, where local features are not discriminative enough and global scene descriptors only provide coarse information. In this talk we present machine learning based techniques that allow us to globally localize autonomous vehicles equipped with 3D Lidar sensors.
Our methods are based on the extraction of segments in 3D point clouds which offers increased invariance to view-point and local structural changes, and facilitates real-time processing of large-scale 3D data. Specifically, we leverage compact data-driven descriptors for performing multiple tasks: global localization, 3D dense map reconstruction, and semantic information extraction.
Additionally, we present novel methods for recognizing places using only a single sparse 3D Lidar scan and for improving the performance of the said descriptors by augmenting them with visual information. We demonstrate the performance of our approaches using multiple experiments based on publicly available autonomous driving datasets (KITTI and NCLT). The implementation is available open-source along with easy to run demonstrations at www.github.com/ethz-asl/segmap
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