# Redesigning SLAM for Arbitrary Multi-Camera Systems

arXiv: https://arxiv.org/abs/2003.02014 · HTML: https://mlsanity.com/paper/2003.02014/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2020.

## Built on by (most cited first, 13 of 13)

- [R3D3: Dense 3D Reconstruction of Dynamic Scenes from Multiple Cameras](https://mlsanity.com/paper/2308.14713/) (2023)
- [Balancing the Budget: Feature Selection and Tracking for Multi-Camera Visual-Inertial Odometry](https://mlsanity.com/paper/2109.05975/) (2021)
- [MAD-ICP: It Is All About Matching Data -- Robust and Informed LiDAR Odometry](https://mlsanity.com/paper/2405.05828/) (2024)
- [Towards Robust Monocular Visual Odometry for Flying Robots on Planetary Missions](https://mlsanity.com/paper/2109.05509/) (2021)
- [A Model for Multi-View Residual Covariances based on Perspective Deformation](https://mlsanity.com/paper/2202.00765/) (2022)
- [MCGS-SLAM: A Multi-Camera SLAM Framework Using Gaussian Splatting for High-Fidelity Mapping](https://mlsanity.com/paper/2509.14191/) (2025)
- [A Collaborative Visual SLAM Framework for Service Robots](https://mlsanity.com/paper/2102.03228/) (2021)
- [Multi-Visual-Inertial System: Analysis, Calibration and Estimation](https://mlsanity.com/paper/2308.05303/) (2023)
- [Dense Dynamic Scene Reconstruction and Camera Pose Estimation from Multi-View Videos](https://mlsanity.com/paper/2603.12064/) (2026)
- [A Pose-only Geometric Constraint for Multi-Camera Pose Adjustment](https://mlsanity.com/paper/2604.23704/) (2026)
- [High-resolution Ecosystem Mapping in Repetitive Environments Using Dual Camera SLAM](https://mlsanity.com/paper/2201.03364/) (2022)
- [Rendering the Directional TSDF for Tracking and Multi-Sensor Registration with Point-To-Plane Scale ICP](https://mlsanity.com/paper/2301.12796/) (2023)
- [StreamRig: Exploiting Intra-Rig Geometry for Streaming Multi-Camera Odometry](https://mlsanity.com/paper/2609.40244/) (2026)

## Abstract (arXiv)

Adding more cameras to SLAM systems improves robustness and accuracy but complicates the design of the visual front-end significantly. Thus, most systems in the literature are tailored for specific camera configurations. In this work, we aim at an adaptive SLAM system that works for arbitrary multi-camera setups. To this end, we revisit several common building blocks in visual SLAM. In particular, we propose an adaptive initialization scheme, a sensor-agnostic, information-theoretic keyframe selection algorithm, and a scalable voxel-based map. These techniques make little assumption about the actual camera setups and prefer theoretically grounded methods over heuristics. We adapt a state-of-the-art visual-inertial odometry with these modifications, and experimental results show that the modified pipeline can adapt to a wide range of camera setups (e.g., 2 to 6 cameras in one experiment) without the need of sensor-specific modifications or tuning.