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project_freya
ilessio-aiflowlab/project_freya
project_freya is a robotics model from ilessio-aiflowlab. Use it for the robotics task on the model card, and read the license before you ship it in a product. It is set up for pytorch. The card lists the license as apache-2.0.
Part of the ANIMA Perception Suite by Robot Flow Labs.
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Public
Updated Apr 2, 2026
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At a glance
- Task
- Robotics
- Library
- pytorch
- License
- apache-2.0
- Access
- Public
- Created
- Mar 28, 2026
- Updated
- Apr 2, 2026
- SHA
- 9433df43
Model card
From the Hugging Face model README
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
- Task
- Robotics
- Library
- pytorch
- License
- apache-2.0
- Created
- Mar 28, 2026
- Updated
- Apr 2, 2026