# LLS-LOAM

**Repository Path**: torchm/LLS-LOAM

## Basic Information

- **Project Name**: LLS-LOAM
- **Description**: Lidar Odometry and Mapping with Mutiple Metrics Linear Least Square ICP
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-03-30
- **Last Updated**: 2021-06-24

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# LLS-LOAM
Lidar Odometry and Mapping with Mutiple Metrics Linear Least Square ICP

## Principle
Instead of using non-linear optimization when doing transformation estimation, this algorithm use the linear least square for all of the point-to-point, point-to-line and point-to-plane distance metrics during the ICP registration process based on a good enough initial guess. 


## How to use

1. Install dependent 3rd libraries: 

[PCL](https://github.com/PointCloudLibrary/pcl), [Eigen](https://eigen.tuxfamily.org/dox/), [Glog](https://github.com/google/glog), [Gflags](https://github.com/gflags/gflags).

If you'd like to use your own data and want to know its absolute projected coordinate, install [Proj](https://proj.org/download.html).

2. Compile
```
mkdir build
cd build
cmake ..
make 
```

3. Run
```
cd ..
# If you'd like to test on kitti dataset
# See below for data preparation
sh script/kitti/kitti_xx.sh
# If you'd like to test on apollo southbay dataset
sh script/apollo/southbay_test.sh
# If you'd like to test on your own dataset, you need to create new shell files and then run it
```

## How to prepare the data
For [KITTI dataset](http://www.cvlibs.net/datasets/kitti/eval_odometry.php), you may refer to [Pointcloud Format Transform Tool](https://github.com/YuePanEdward/Pointcloud_Format_Transformer).

After transforming the point cloud from .bin to .pcd, rename the point cloud folder HDL64.

Horizontally, create a folder called OXTS, and put the ground truth pose .txt file (which can be download from kitti's website) into it.

Then create a pcd file list:
```
touch file_list.txt
ls HDL64 >> file_list.txt
```

Then the data is ready for test.

For Baidu Appollo Dataset, you should download the testing data 'Apollo-SouthBay' from [Baidu Apollo Data Platform](http://data.apollo.auto/?locale=en-us&lang=en). The data preparation is like kitti's. Remember to use the tool 'rename_number_pcd_files.sh' to regularize the pcd file names (for example, from 1.pcd to 0001.pcd).

For your own dataset, if the point cloud are in pcd format and the pose and imu information are in oxts format, then you can directly use this programme (LoadPcImuGnss Method). Or you need to transform the data youself.

The common folder structure should be
```
_____base_folder
     |___HDL64
     .   |____0000.pcd
     .   |____0001.pcd
     .   |____.....pcd
     |___OXTS
     .   |____pose.txt
     |___file_list.txt
```

## TO DO LIST

For the loop closure, pose graph optimization and collaboration mapping module, the code has not been released yet.

The workaround is here: [CloudControlNet](https://github.com/YuePanEdward/CloudControlNet/tree/dev)

- [ ] Speed up

- [ ] Solve the serious Z drift problem

- [ ] Use DL to remove the dynamic objects

- [ ] Add ROS module

- [ ] Tech report or paper


## Demo

### On KITTI dataset

#### Yellow: Lidar Odometry Position, Purple: Ground Truth Position

#### seq 00
![alt text](assets/kitti_00_pointcloud.png)

#### seq 01
![alt text](assets/kitti_01_pointcloud.png)

#### seq 02
![alt text](assets/kitti_02_pointcloud.png)

#### seq 03
![alt text](assets/kitti_03_pointcloud.png)

#### seq 04
![alt text](assets/kitti_04_pointcloud.png)

#### seq 05
![alt text](assets/kitti_05_pointcloud.png)

#### seq 06
![alt text](assets/kitti_06_pointcloud.png)

#### seq 07
![alt text](assets/kitti_07_pointcloud.png)

#### seq 08
![alt text](assets/kitti_08_pointcloud.png)

#### seq 09
![alt text](assets/kitti_09_pointcloud.png)

#### seq 10
![alt text](assets/kitti_10_pointcloud.png)

#### Quantity Evaluation on KITTI dataset

| seq | ATE(%) | ARE(0.01deg/m) | TPF(ms/frame) |
|-----|-----|-----|-----|
| 00  | 1.362     | 0.603    | 193.4    |
| 01  | 4.175     | 1.076    | 183.8    |
| 02  | 2.210     | 0.928    | 196.8    |
| 03  | 1.282     | 1.037    | 181.5    |
| 04  | 2.108     | 0.981    | 194.3    |
| 05  | 1.453     | 0.677    | 189.3    |
| 06  | 1.119     | 0.609    | 188.6    |
| 07  | 0.763     | 0.505    | 198.1    |
| 08  | 1.834     | 0.800    | 187.4    |
| 09  | 2.293     | 1.008    | 191.2    |
| 10  | 2.692     | 0.905    | 190.4    |

### Apollo SouthBay dataset

![alt text](assets/southbay_test_1.png)

### On own dataset

![alt text](assets/demo_own_dataset_1.png)

![alt text](assets/demo_own_dataset_highway.png)