  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. For additional information, please see the project website at:
https://www.cs.toronto.edu/~ajyang/amv-slam
