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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Trent Tesoro May 2026

As of 2022, Trent Tesoro continues to compete in various stock car racing events, including the NASCAR Gander Outdoors Truck Series and the ARCA Menards Series.

In 2015, Tesoro joined the NASCAR K&N Pro Series East, driving for the Venturini Motorsports team. He scored several top-10 finishes and won his first K&N Pro Series East pole at the New Hampshire Motor Speedway.

Tesoro transitioned to stock car racing in 2014, competing in the ARCA Racing Series and the NASCAR K&N Pro Series. He made his ARCA debut at the 2014 ARCA Southern Nationals at the Memphis International Speedway.


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

As of 2022, Trent Tesoro continues to compete in various stock car racing events, including the NASCAR Gander Outdoors Truck Series and the ARCA Menards Series.

In 2015, Tesoro joined the NASCAR K&N Pro Series East, driving for the Venturini Motorsports team. He scored several top-10 finishes and won his first K&N Pro Series East pole at the New Hampshire Motor Speedway.

Tesoro transitioned to stock car racing in 2014, competing in the ARCA Racing Series and the NASCAR K&N Pro Series. He made his ARCA debut at the 2014 ARCA Southern Nationals at the Memphis International Speedway.


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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