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slamplay is a collection of powerful tools to start playing and experimenting with SLAM in C++
| Date | Stars |
|---|---|
| 2026-07-24 | 445 |
| 2026-07-25 | 445 |
| 2026-07-28 | 445 |
| 2026-07-30 | 445 |
| 2026-08-06 | 445 |
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<p align="center">
<img src="images/slamplay-logo.png"
alt="slamplay" height="120" border="0"/>
</p>
# slamplay
Author: [Luigi Freda](https://www.luigifreda.com)
<!-- TOC depthFrom:2 depthTo:4 -->
- [slamplay](#slamplay)
- [Repository layout](#repository-layout)
- [How to start](#how-to-start)
- [Configuration](#configuration)
- [Install data](#install-data)
- [Deep learning (DL)](#deep-learning-dl)
- [Install DL models](#install-dl-models)
- [GPU support with `CUDA`, `cuDNN`, `TensorRT`](#gpu-support-with-cuda-cudnn-tensorrt)
- [Install tensorflow C++ API](#install-tensorflow-c-api)
- [Docker](#docker)
- [Full SLAM](#full-slam)
- [Visual SLAM (vSLAM)](#visual-slam-vslam)
- [VSLAM datasets](#vslam-datasets)
- [LiDAR-inertial SLAM (LIO)](#lidar-inertial-slam-lio)
- [LIO datasets](#lio-datasets)
- [Eigen Tutorials](#eigen-tutorials)
- [Front-end](#front-end)
- [Features DL (Deep Learning)](#features-dl-deep-learning)
- [Depth DL](#depth-dl)
- [Tensorflow C++ API](#tensorflow-c-api)
- [Back-end](#back-end)
- [GTSAM examples](#gtsam-examples)
- [Ceres examples](#ceres-examples)
- [g2o examples](#g2o-examples)
- [IO](#io)
- [chrono](#chrono)
- [Profiling](#profiling)
- [Tracy](#tracy)
- [Credits](#credits)
- [License](#license)
<!-- /TOC -->
**slamplay** is a collection of tools to start playing and experimenting with **SLAM in C++**. It installs and wires up, in a single CMake framework, some of the most important
- back-end frameworks (*g2o*, *gtsam*, *ceres*, *se-sync*, etc.),
- front-end tools (*OpenCV*, *PCL*, lidar/IMU processing, etc.),
- algebra and geometry libs (*eigen*, *sophus*, *cholmod*, etc.),
- viz tools (*pangolin*, *imgui*, *rerun*, etc.),
- loop-closure frameworks (*DBoW2*, *DBoW3*, *iBoW*, etc.),
- deep learning tools (*TensorRT*, *tensorflow_cc*, *libtorch*, *onnxruntime*, etc.),
along with commented examples to get started quickly.
I created **slamplay** for a computer vision class I taught. I started developing it for fun, during my free time, taking inspiration from some repos available on the web.
<p align="center">
<img src="images/kitti-VO.png"
alt="KITTI visual odometry" height="180" border="0"/>
<img src="images/euroc-VO.png"
alt="EUROC VO" height="180" border="0"/>
<img src="images/lio-slam.png"
alt="LIO SLAM" height="180" border="0"/>
<img src="images/lio-localization.png"
alt="LIO localization" height="180" border="0"/>
</p>
<p align="center">
<img src="images/direct-method.png"
alt="KITTI direct method for feature tracking" height="180" border="0"/>
<img src="images/clouds-viz.png"
alt="Pointcloud visualization" height="180" border="0"/>
<img src="images/slamplay-depth-anything.png"
alt="Pointcloud visualization of DepthAnythingV2" height="180" border="0"/>
<img src="images/slamplay-kitti-sam.png"
alt="Segment Anything Model on Kitti" height="180" border="0"/>
<img src="images/slamplay-segment-anything.png"
alt="Segment Anything Model" height="180" border="0"/>
</p>
## Repository layout
| Folder | Role |
|--------|------|
| `algebra_geometry` | Eigen / geometry tutorials and examples |
| `backend` | *g2o*, *gtsam*, *ceres*, *se-sync* examples |
| `config` | YAML configs (`vslam/`, `lio_slam/`) |
| `core` | Shared libraries: DL models, `ad/` lidar–IMU stack |
| `data` | Datasets and sample assets |
| `dense_mapping` | Dense / surfel mapping examples |
| `docs` | Extra documentation |
| `frontend` | Vision and sensor front-end examples **(*)** |
| `full_slam` | End-to-end SLAM: `vslam/`, `lio_slam/`, apps |
| `io` | I/O utilities |
| `loop_closure` | Place-recognition examples |
| `ros` | ROS-compat modules (no system ROS required) |
| `results` | Default output for mapping runs |
| `scripts` | Helper scripts |
| `semantics` | Semantic segmentation **(*)** |
| `utils` | Misc utilities |
| `viz` | Visualization tools |
**(*)** C++ tools based on *TensorRT*, *tensorflow_cc*, *onnxrExcerpt of 13,345 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:ba929db251a9a737, topic:slam, desc:slam, readme:slam
matched fp:ba929db251a9a737, topic:deep-learning
matched fp:ba929db251a9a737, topic:object-detection, readme:computer vision, readme:semantic segmentation