project_slam_mipslam

SLAM-MIPSLAM — ANIMA Module

Part of the ANIMA Perception Suite by Robot Flow Labs.

Paper

MipSLAM: Alias-Free Gaussian Splatting SLAM (arXiv:2603.06989)

Yingzhao Li, Yan Li, Shixiong Tian, Yanjie Liu, Lijun Zhao, Gim Hee Lee

Architecture

MipSLAM is an RGB-D 3D Gaussian Splatting SLAM system with two key innovations:

1. Elliptical Adaptive Anti-aliasing (EAA) — alias-free rendering under changed camera configurations via importance-weighted elliptical quadrature (Eq. 5-6)
2. Spectral-Aware Pose Graph Optimization (SA-PGO) — trajectory drift reduction using frequency-domain analysis and graph Laplacian decomposition (Eq. 8-14)

The system tracks RGB-D frames, builds a dense Gaussian map, and supports re-rendering at arbitrary resolutions without aliasing artifacts.

Exported Formats

Evaluation Results (Replica Benchmark)

Paper target: ATE RMSE 0.28 cm (with iterative pose refinement not yet implemented).

Usage

import torch
from safetensors.torch import load_file

# Load Gaussian map
data = load_file("pytorch/slam_mipslam_v1.safetensors")
means = data["gaussian_means"]      # [N, 3]
covs = data["gaussian_covs"]        # [N, 3, 3]
opacities = data["gaussian_opacities"]  # [N]
colors = data["gaussian_colors"]    # [N, 3]
trajectory = data["trajectory"]     # [T, 4, 4]

CUDA Kernels

Shared EAA CUDA kernel available at: /mnt/forge-data/shared_infra/cuda_extensions/eaa_renderer/

from eaa_renderer import eaa_render
rgb, depth, alpha = eaa_render(means2d, covs2d, depths, opacities, colors, H=480, W=640)

Training

• Hardware: NVIDIA L4 (23 GB VRAM)
• Framework: PyTorch 2.11 + custom EAA CUDA kernel
• Dataset: Replica RGB-D (6 sequences, 500 frames each, 480×640)
• Config: See configs/training.toml

Docker

docker compose -f docker-compose.serve.yml --profile serve up -d
curl http://localhost:8080/health

License

Apache 2.0 — Robot Flow Labs / AIFLOW LABS LIMITED