Asynchronous Multi-View SLAM
Anqi Joyce Yang, Can Cui, Ioan Andrei Bârsan, Raquel Urtasun, Shenlong Wang
Abstract
Existing multi-camera SLAM systems assume synchronized shutters for all cameras, which is often not the case in practice. In this work, we propose a generalized multi-camera SLAM formulation which accounts for asynchronous sensor observations. Our framework integrates a continuous-time motion model to relate information across asynchronous multi-frames during tracking, local mapping, and loop closing. For evaluation, we collected AMV-Bench, a challenging new SLAM dataset covering 482 km of driving recorded using our asynchronous multi-camera robotic platform. AMV-Bench is over an order of magnitude larger than previous multi-view HD outdoor SLAM datasets, and covers diverse and challenging motions and environments. Our experiments emphasize the necessity of asynchronous sensor modeling, and show that the use of multiple cameras is critical towards robust and accurate SLAM in challenging outdoor scenes. The supplementary material is located at: https://www.cs.toronto.edu/~ajyang/amv-slam
BibTeX
@inproceedings{icra2021_asynchronousmult,
title = {Asynchronous Multi-View SLAM},
author = {Anqi Joyce Yang and Can Cui and Ioan Andrei Bârsan and Raquel Urtasun and Shenlong Wang},
booktitle = {ICRA 2021},
year = {2021}
}