# Asynchronous Multi-View SLAM

arXiv: https://arxiv.org/abs/2101.06562 · HTML: https://mlsanity.com/paper/2101.06562/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 88 references that are in the map.
- First posted on arXiv 17 Jan 2021.

## Builds on (most cited first, 28 of 28)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://mlsanity.com/paper/1610.06475/) (2016)
- [TartanAir: A Dataset to Push the Limits of Visual SLAM](https://mlsanity.com/paper/2003.14338/) (2020)
- [Direct Sparse Odometry](https://mlsanity.com/paper/1607.02565/) (2016)
- [Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age](https://mlsanity.com/paper/1606.05830/) (2016)
- [D2-Net: A Trainable CNN for Joint Detection and Description of Local Features](https://mlsanity.com/paper/1905.03561/) (2019)
- [A2D2: Audi Autonomous Driving Dataset](https://mlsanity.com/paper/2004.06320/) (2020)
- [LF-Net: Learning Local Features from Images](https://mlsanity.com/paper/1805.09662/) (2018)
- [LDSO: Direct Sparse Odometry with Loop Closure](https://mlsanity.com/paper/1808.01111/) (2018)
- [4Seasons: A Cross-Season Dataset for Multi-Weather SLAM in Autonomous Driving](https://mlsanity.com/paper/2009.06364/) (2020)
- [Semantic Instance Annotation of Street Scenes by 3D to 2D Label Transfer](https://mlsanity.com/paper/1511.03240/) (2015)
- [Ford Multi-AV Seasonal Dataset](https://mlsanity.com/paper/2003.07969/) (2020)
- [Efficient Derivative Computation for Cumulative B-Splines on Lie Groups](https://mlsanity.com/paper/1911.08860/) (2019)
- [Efficient Continuous-Time SLAM for 3D Lidar-Based Online Mapping](https://mlsanity.com/paper/1810.06802/) (2018)
- [RF-Net: An End-to-End Image Matching Network based on Receptive Field](https://mlsanity.com/paper/1906.00604/) (2019)
- [EU Long-term Dataset with Multiple Sensors for Autonomous Driving](https://mlsanity.com/paper/1909.03330/) (2019)
- [Direct Sparse Odometry with Rolling Shutter](https://mlsanity.com/paper/1808.00558/) (2018)
- [MultiCol-SLAM - A Modular Real-Time Multi-Camera SLAM System](https://mlsanity.com/paper/1610.07336/) (2016)
- [Batch Nonlinear Continuous-Time Trajectory Estimation as Exactly Sparse Gaussian Process Regression](https://mlsanity.com/paper/1412.0630/) (2014)
- [Trajectory Representation and Landmark Projection for Continuous-Time Structure from Motion](https://mlsanity.com/paper/1805.02543/) (2018)
- [A White-Noise-On-Jerk Motion Prior for Continuous-Time Trajectory Estimation on SE(3)](https://mlsanity.com/paper/1809.06518/) (2018)
- [Spline Error Weighting for Robust Visual-Inertial Fusion](https://mlsanity.com/paper/1804.04820/) (2018)
- [DA4AD: End-to-End Deep Attention-based Visual Localization for Autonomous Driving](https://mlsanity.com/paper/2003.03026/) (2020)

## Built on by (most cited first, 12 of 12)

- [BARF: Bundle-Adjusting Neural Radiance Fields](https://mlsanity.com/paper/2104.06405/) (2021)
- [Multi-View Masked World Models for Visual Robotic Manipulation](https://mlsanity.com/paper/2302.02408/) (2023)
- [Local-to-Global Registration for Bundle-Adjusting Neural Radiance Fields](https://mlsanity.com/paper/2211.11505/) (2022)
- [Towards Optimal Strategies for Training Self-Driving Perception Models in Simulation](https://mlsanity.com/paper/2111.07971/) (2021)
- [Continuous-Time vs. Discrete-Time Vision-based SLAM: A Comparative Study](https://mlsanity.com/paper/2202.08894/) (2022)
- [AFT-VO: Asynchronous Fusion Transformers for Multi-View Visual Odometry Estimation](https://mlsanity.com/paper/2206.12946/) (2022)
- [Self-Improving SLAM in Dynamic Environments: Learning When to Mask](https://mlsanity.com/paper/2210.08350/) (2022)
- [Hyperion -- A fast, versatile symbolic Gaussian Belief Propagation framework for Continuous-Time SLAM](https://mlsanity.com/paper/2407.07074/) (2024)
- [AsynEIO: Asynchronous Monocular Event-Inertial Odometry Using Gaussian Process Regression](https://mlsanity.com/paper/2411.12175/) (2024)
- [Jacobian Computation for Cumulative B-Splines on SE(3) and Application to Continuous-Time Object Tracking](https://mlsanity.com/paper/2201.10602/) (2022)
- [Self-Aligning Depth-regularized Radiance Fields for Asynchronous RGB-D Sequences](https://mlsanity.com/paper/2211.07459/) (2022)
- [FA-BARF: Frequency Adapted Bundle-Adjusting Neural Radiance Fields](https://mlsanity.com/paper/2503.12086/) (2025)

## Abstract (arXiv)

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