Real Time Monocular Scene Understanding and Multibody SLAM in Bird’s Eye View

Abstract

The field of autonomous driving has seen many advances in recent times. In this thesis, we try to look at some important challenges in autonomous driving from the perspective of a single (monocular) camera: Real-Time Scene (Road/Lanes) Layout and multi-object simultaneous localisation and mapping in the outdoor scene. To tackle the challenges faced by monocular setups/systems (such as scale ambiguity in monocular reconstruction, dynamic object localisation, and uncertainty in feature representation), we transpose the above problems into orthographic (bird’s-eye view) space. Thus, we perform road/lane layout reconstruction, localisation and mapping of dynamic participants and the ego camera vehicle in the scene with an orthographic view as the configuration space. By assuming only the height of the egocamera above the ground, we leverage single-view metrology cues to accurately localise the ego-vehicle and all other traffic participants in bird’s-eye view. We demonstrate that our system outperforms prior work that uses strictly greater information, highlighting each design decision’s relevance via an ablation analysis