Asynchronous Multi-View SLAM

TL;DR

This paper introduces a generalized multi-camera SLAM system that effectively handles asynchronous sensor observations, improving robustness and accuracy in outdoor environments with diverse motions.

Contribution

It proposes a novel SLAM framework that models asynchronous multi-camera data using a continuous-time motion model, along with a new large-scale dataset for evaluation.

Findings

Asynchronous sensor modeling is essential for robust SLAM.

Multiple cameras significantly improve SLAM performance in challenging scenes.

The proposed system outperforms traditional synchronized approaches.

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