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
翻译:在这项工作中,我们提出一个通用的多镜头SLAM-Bench的配方,其中考虑到非同步传感器观测。我们的框架整合了一个连续时间运动模型,以便在跟踪、本地绘图和环圈关闭期间将信息跨非同步多框架;为了评估,我们收集了AMV-Bench,这是一个具有挑战性的新的SLM-Bench数据集,覆盖了482公里的驾驶器,使用我们的非同步多镜头机器人平台记录了482公里的驾驶器。AMV-Bench是一个规模大于以往多视图的HD室外SLM数据集,涵盖各种具有挑战性的行动和环境。我们的实验强调无同步传感器建模的必要性,并表明在具有挑战性的户外场景区使用多摄像头对于稳健和准确的SLM至关重要。其他信息请见项目网站:https://www.cs.torontoto.edu/yajyang/amv-slamm。