Gaussian-LIC2: LiDAR-Inertial-Camera Gaussian Splatting SLAM
Paper • 2507.04004 • Published
Part of the ANIMA Perception Suite by Robot Flow Labs.
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
Tightly-coupled LiDAR+Inertial+Camera Gaussian Splatting SLAM:
| 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 |
| 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%) |
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")
@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}
}
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