# BALM

**Repository Path**: sungq5/BALM

## Basic Information

- **Project Name**: BALM
- **Description**: 雷达BA
- **Primary Language**: C++
- **License**: GPL-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-09-10
- **Last Updated**: 2024-09-10

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# BALM 2.0

## Efficient and Consistent Bundle Adjustment on Lidar Point Clouds

**Note: If your initial pose error is too large in real-world datasets, the plane detection module may not find enough planes (point association) to BA optimization. Our plane determination criteria may be strict for huge initial pose errors. So before optimization, please to check the planes (which are displayed in our implementation). If you really cannot provide better initial poses and want to get accurate results, you can try the process of "coarse-to-fine", namely from large voxel size and loose plane determination criteria, to small voxel size and strict plane determination criteria, using multiple optimization with different point associations.** 

**BALM 2.0** is a basic and simple system to use bundle adjustment (BA) in lidar mapping. It includes three experiments in the paper. We try to keep the code as concise as possible, to avoid confusing the readers. It is notable that this package does not include the application experiments, which will be open-sourced in other projects. The paper  is available on Arxiv and more experiments details can be found in the [video](https://youtu.be/MDrIAyhQ-9E).

<div align="center">
    <img src="./pic/formulation.png" width = 50% >
</div>

**Related papers:**

[Efficient and Consistent Bundle Adjustment on Lidar Point Clouds](https://arxiv.org/abs/2209.08854)

[BALM: Bundle Adjustment for Lidar Mapping](https://ieeexplore.ieee.org/abstract/document/9366383)

## 1. Prerequisited

### 1.1 Ubuntu and ROS

Ubuntu 64-bit 20.04.  [ROS Installation](http://wiki.ros.org/ROS/Installation). (Noetic recommended)

### 1.2 PCL and Eigen

Follow [PCL Installation](https://pointclouds.org/) (1.10 recommended)

Follow [Eigen Installation](https://eigen.tuxfamily.org/index.php?title=Main_Page) (3.3.7 recommended)

## 2. Build

Clone the repository and catkin_make:

```
cd ~/catkin_ws/src
git clone https://github.com/hku-mars/BALM
cd ../
catkin_make
source ~/catkin_ws/devel/setup.bash
```

**Note**: Before compilation, the file folder "BALM-old" had better be deleted if you do not require BALM1.0, or removed to other irrelevant path.

## 3. Run the package

### 3.1 Consistency experiments

```
roslaunch balm2 consistency.launch
```

### 3.2 Benchmark on virtual point cloud

```
roslaunch balm2 benchmark_virtual.launch
```

### 3.3 Benchmark on real-world dataset

```
roslaunch balm2 benchmark_realworld.launch
```

You can also use the right update by: 

![right_update](./pic/right_update.png)

## 4. Applications

1. **Lidar-Inertial odometry with sliding window optimization**:  The codes will be open-sourced in the next work.
2. **Multiple-Lidar calibration**: The github of [MLCC](https://github.com/hku-mars/mlcc).
3. **Global BA on large-scale dataset**: The github of  [HBA](https://github.com/hku-mars/HBA).

## 5. Acknowledgement

In the development of this package, we refer to [FAST-LIO2](https://github.com/hku-mars/FAST_LIO), [Hilti](https://www.hilti-challenge.com/), [VIRAL](https://ntu-aris.github.io/ntu_viral_dataset/) and [UrbanLoco](https://github.com/weisongwen/UrbanLoco) for source codes or datasets.