Asynchronous Multi-View SLAM
This work addresses a practical limitation in SLAM systems for robotics and autonomous driving by handling asynchronous multi-camera data, though it is incremental as it builds on existing multi-view SLAM methods.
The paper tackles the problem of multi-camera SLAM with asynchronous sensors, proposing a generalized formulation that integrates a continuous-time motion model, and demonstrates its necessity and effectiveness on a new large-scale dataset, showing improved robustness and accuracy in challenging outdoor scenes.
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