Asynchronous Multi-View SLAM

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

Authors

• Anqi Joyce Yang
• Can Cui
• Ioan Andrei Bârsan
• Raquel Urtasun
• Shenlong Wang

Keywords

• Tracking loops
• Simultaneous localization and mapping
• Automation
• Tracking
• Conferences
• Robot vision systems
• Cameras
• Synchronization
• Diverse Environments
• Local Map
• Motion Model
• Previous Datasets
• Multiple Cameras
• Continuous-time Model
• Loop Closure
• Linear Model
• Field Of View
• Cubic Spline
• Large-scale Datasets
• Supplementary Materials For Details
• Localization Error
• Motion Parameters
• Pose Estimation
• Rigid Transformation
• Lie Algebra
• Levenberg-Marquardt Algorithm
• Map Points
• Bundle Adjustment
• Reprojection Error
• Wide Field Of View
• World Frame
• Coordinate Frame
• Arbitrary Time
• Stereo Pairs
• Pinhole Camera
• Fisheye Lens