Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A Modular Framework for LiDAR-based Lifelong Mapping
| Date | Stars |
|---|---|
| 2026-07-24 | 532 |
| 2026-07-25 | 532 |
| 2026-07-28 | 532 |
| 2026-07-30 | 532 |
| 2026-08-06 | 532 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# LT-mapper: A Modular Framework for LiDAR-based Lifelong Mapping <!-- ------------------------------------------ --> <!-- ## Overview --> **LT-mapper** is an open-source, modular framework designed for **LiDAR-based lifelong mapping** in dynamic environments. It enables robots to maintain accurate 3D maps over long time scales, addressing challenges such as: - Multi-session trajectory alignment (LT-SLAM) - High/low dynamic object filtering and change detection (LT-removert) - Efficient map management and change composition (LT-map) <p align="center"><img src="./doc/challenges.png" width=1000></p> The framework supports real-world scenarios, handling ephemeral and persistent changes across multiple sessions without requiring perfect initial alignment. For more deatils, please refer to our [paper](./doc/ltmapper.pdf) and [video](https://youtu.be/vlYKfp1p2j8). <!-- ### Why LT-mapper? --> <!-- - For LiDAR-based long-term mapping, three challenges exist. --> <!-- <p align="center"><img src="doc/whyltmapper.png" width=1000></p> --> ## Features - **Multi-Session SLAM (LT-SLAM):** Align multiple sessions in a shared coordinate system using anchor-node-based pose graph optimization. - **Dynamic Object Removal (LT-removert):** Efficiently filter high dynamic points and detect low-dynamic changes between sessions. - **Map Management (LT-map):** Maintain live maps, generate meta-maps, and efficiently compose changes using delta maps. - **Seamless Modular Integration:** Each module can run seperately via file-based in/out protocol. <p align="center"><img src="./doc/pipeline.png" width=630></p> <!-- ------------------------------------------ --> ## Getting Started ### Prerequisites - ROS (tested with Melodic and Noetic) ```shell sudo apt install -y ros-distro-navigation sudo apt install -y ros-distro-robot-localization sudo apt install -y ros-distro-robot-state-publisher ``` - GTSAM ```shell sudo add-apt-repository ppa:borglab/gtsam-release-4.0 sudo apt install -y libgtsam-dev libgtsam-unstable-dev ``` ### Installation #### Clone the repository ```shell cd ~/catkin_ws/src git clone https://github.com/gisbi-kim/lt-mapper ``` #### Build the package ```shell cd .. catkin build ltslam removert ``` #### Using Docker ```shell cd ~/catkin_ws/src/lt-mapper/docker bash build.sh bash run.sh ``` or you can pull the pre-built image from Docker Hub. ```shell docker pull dongjae0107/lt-mapper:latest bash run.sh ``` <!-- ------------------------------------------ --> ## How to use it ### 0. Single-session Data Generation To run the LT-mapper, you first need to generate a set of session data containing the following files: - **Keyframe Point Clouds** - **Keyframe Scan Context Descriptors (SCDs)** - **Pose Graph File** You can generate these session data files using the [saver tool](https://github.com/gisbi-kim/SC-LIO-SAM#applications) provided with [SC-LIO-SAM](https://github.com/gisbi-kim/SC-LIO-SAM). Alternatively, the saver is also available in [SC-A-LOAM](https://github.com/gisbi-kim/SC-A-LOAM) and [FAST_LIO_SLAM](https://github.com/gisbi-kim/FAST_LIO_SLAM). ### 1. LT-SLAM **LT-SLAM** module aligns trajectories across multiple sessions. For a step-by-step guide, check out this [tutorial video](https://youtu.be/BXBTVurNToU). ```shell # modify paths in ltslam/config/params.yaml before you run roslaunch ltslam run.launch ``` ### 2. LT-removert & LT-map **LT-removert & LT-map** module utilize [Removert](https://github.com/gisbi-kim/removert) to clean single-session data and detect changes between two sessions. ```shell # modify paths in ltremovert/config/params_ltmapper.yaml before you run roslaunch removert run_ltmapper.launch ``` **Outputs** Once the process is complete, following outputs will be generated: <p align="center"><img src="./doc/outputs.png" width=600></p> <!-- ------------------------------------------ --> ## Example: LT-mapper on ParkingLot dataset You can download the ParkingLot dataset from this [link](
Excerpt of 5,685 characters
Read on GitHub
10
9
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:5988def404702f89, topic:slam, readme:slam