FREYA — Gaussian-LIC2 SLAM (ANIMA Module)

Part of the ANIMA Perception Suite by Robot Flow Labs.

Paper

Gaussian-LIC2: LiDAR-Inertial-Camera Gaussian Splatting SLAM (arXiv:2507.04004)

Xiaolei Lang, Jiajun Lv, Kai Tang, Laijian Li, Jianxin Huang, Lina Liu, Yong Liu, Xingxing Zuo

Architecture

Tightly-coupled LiDAR+Inertial+Camera Gaussian Splatting SLAM:

  • Continuous-time B-spline SE(3) trajectory optimization
  • Incremental 3D Gaussian map with SH lighting (degree 3)
  • Zero-shot depth completion for LiDAR-blind areas
  • CUDA-accelerated tile-based rendering
  • Per-scene optimization (not batch training)

Exported Formats

Format File Use Case
PyTorch (.pth) pytorch/freya_v1.pth Training, fine-tuning
SafeTensors pytorch/freya_v1.safetensors Fast loading, safe
ONNX onnx/freya_depth_v1.onnx Cross-platform depth inference
TensorRT FP16 tensorrt/freya_depth_v1_fp16.engine Edge deployment (Jetson/L4)
TensorRT FP32 tensorrt/freya_depth_v1_fp32.engine Full precision inference
Checkpoint checkpoints/best.pth Resume SLAM optimization
Config configs/training.toml Reproducibility
Logs logs/training_history.json Loss curves, metrics

Training Details

Parameter Value
Hardware NVIDIA L4 (23GB VRAM)
Dataset TUM VI Benchmark (room1, 2821 frames)
Gaussians 12.2M pre-allocated, 500K active
Optimizer Adam (per-param, foreach=False)
Mixed Precision bf16
Best Loss 0.0010
VRAM Usage 17.8GB (77%)

Usage

import torch

state = torch.load("pytorch/freya_v1.pth", weights_only=False)
gaussian_map = state["gaussian_map"]
trajectory = state["trajectory"]

# SafeTensors
from safetensors.torch import load_file
tensors = load_file("pytorch/freya_v1.safetensors")

Citation

@article{lang2025gaussianlic2,
  title={Gaussian-LIC2: LiDAR-Inertial-Camera Gaussian Splatting SLAM},
  author={Lang, Xiaolei and Lv, Jiajun and Tang, Kai and Li, Laijian and Huang, Jianxin and Liu, Lina and Liu, Yong and Zuo, Xingxing},
  year={2025},
  journal={arXiv preprint arXiv:2507.04004}
}

License

Apache 2.0 — Robot Flow Labs / AIFLOW LABS LIMITED

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