MCGS-SLAM

A Multi-Camera SLAM Framework Using Gaussian Splatting for High-Fidelity Mapping

Anonymous Author

SLAM System Pipeline

Our method performs real-time SLAM by fusing synchronized inputs from a multi-camera rig into a unified 3D Gaussian map. It first selects keyframes and estimates depth and normal maps for each camera, then jointly optimizes poses and depths via multi-camera bundle adjustment and scale-consistent depth alignment. Refined keyframes are fused into a dense Gaussian map using differentiable rasterization, interleaved with densification and pruning. An optional offline stage further refines camera trajectories and map quality. The system supports RGB inputs, enabling accurate tracking and photorealistic reconstruction.

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Brothers-a-tale-of-two-sons.rar

Most obstacles require both brothers. For example, the older brother might need to distract a guard while the younger brother sneaks past, or they may need to swing together on a rope.

Controlled with the Left Analog Stick and Left Trigger (L2/LT) . He is stronger and can pull heavy levers or boost the younger brother to higher ledges.

The younger brother is afraid of water. When crossing rivers, have the older brother swim first; the younger brother must grab onto his back using his interaction trigger (RT/R2) to be ferried across. 100% Achievement Guide (Missable Secrets)


Analysis of Single-Camera and Multi-Camera SLAM (Mapping)

Most obstacles require both brothers. For example, the older brother might need to distract a guard while the younger brother sneaks past, or they may need to swing together on a rope.

Controlled with the Left Analog Stick and Left Trigger (L2/LT) . He is stronger and can pull heavy levers or boost the younger brother to higher ledges.

The younger brother is afraid of water. When crossing rivers, have the older brother swim first; the younger brother must grab onto his back using his interaction trigger (RT/R2) to be ferried across. 100% Achievement Guide (Missable Secrets)


Analysis of Single-Camera and Multi-Camera SLAM (Tracking)

In this section, we benchmark tracking accuracy across eight driving sequences from the Waymo dataset (Real World). MCGS-SLAM achieves the lowest average ATE, significantly outperforming single-camera methods.
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We further evaluate tracking on four sequences from the Oxford Spires dataset (Real World). MCGS-SLAM consistently yields the best performance, demonstrating robust trajectory estimation in large-scale outdoor environments.
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