---
tags:
  - robotics
  - anima
  - freya
  - gaussian-splatting
  - slam
  - lidar
  - 3d-reconstruction
  - sensor-fusion
  - robot-flow-labs
library_name: pytorch
pipeline_tag: robotics
license: apache-2.0
---

# FREYA — Gaussian-LIC2 SLAM (ANIMA Module)

Part of the [ANIMA Perception Suite](https://github.com/RobotFlow-Labs) 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

```python
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

```bibtex
@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