학술논문
BEV-SLAM: Building a Globally-Consistent World Map Using Monocular Vision
Document Type
Conference
Source
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Intelligent Robots and Systems (IROS), 2022 IEEE/RSJ International Conference on. :3830-3836 Oct, 2022
Subject
Language
ISSN
2153-0866
Abstract
The ability to produce large-scale maps for nav-igation, path planning and other tasks is a crucial step for autonomous agents, but has always been challenging. In this work, we introduce BEV-SLAM, a novel type of graph-based SLAM that aligns semantically-segmented Bird's Eye View (BEV) predictions from monocular cameras. We introduce a novel form of occlusion reasoning into BEV estimation and demonstrate its importance to aid spatial aggregation of BEV predictions. The result is a versatile SLAM system that can operate across arbitrary multi-camera configurations and can be seamlessly integrated with other sensors. We show that the use of multiple cameras significantly increases performance, and achieves lower relative error than high-performance GPS. The resulting system is able to create large, dense, globally-consistent world maps from monocular cameras mounted around an ego vehicle. The maps are metric and correctly-scaled, making them suitable for downstream navigation tasks.