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Alexander Schaefer
2019-07-25 10:28:10 +02:00
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# Long-Term Urban Vehicle Localization Using Pole Landmarks Extracted from 3-D Lidar Scans
> **NOTE**
>
> In its current version, this repository is a preview.
> We will publish the complete implementation of the localization system described in the paper "Long-Term Urban Vehicle Localization Using Pole Landmarks Extracted from 3-D Lidar Scans" if the paper is accepted.
This repository contains the Python code that accompanies our paper ["Long-Term Urban Vehicle Localization Using Pole Landmarks Extracted from 3-D Lidar Scans"](https://www.ecmr2019.eu/) submitted to the [European Conference on Mobile Robots](https://www.ecmr2019.eu/).
This repository contains the Python code that accompanies our paper ["Long-Term Urban Vehicle Localization Using Pole Landmarks Extracted from 3-D Lidar Scans"](http://ais.informatik.uni-freiburg.de/publications/papers/schaefer19ecmr.pdf) submitted to the [European Conference on Mobile Robots](https://www.ecmr2019.eu/).
The implementation allows to
* extract the parameters of pole-like objects from 3-D lidar scans,
@@ -58,33 +52,5 @@ sudo apt install python-pip python-tk
and use it to install the following Python packages:
```bash
pip install numpy matplotlib open3d-python progressbar pyquaternion transforms3d scipy networkx psutil
pip install numpy matplotlib open3d-python progressbar pyquaternion transforms3d scipy scikit-image networkx psutil
```
## NCLT ground-truth optimization
For the experiments on NCLT described in the paper, we rely on the ground-truth poses given the by authors.
Due to the way they were created, these poses are however quite noisy.
To deal with this issue, an optimized ground-truth for NCLT can be generated separately for the trajectory of each session using ICP matching of the Velodyne scans.
First a pose graph is created with
```bash
python ncltgtopt.py 2012-01-08
```
The pose graph consists of one node for each odometry measurement (interpolated to the Velodyne time stamps) and corresponding edges.
The original NCLT ground truth poses and covariances are added as edges from an additional origin node at `(0,0,0)`.
Further, ICP scan matching edges are added for each node to its neighbors.
The pose graph is split into chunks and is optimized and merged with:
```bash
python spltoptpg.py 2012-01-08
```
Example images of point clouds registered with the original NCLT ground truth and with our optimized ground truth make the considerable noise in the original ground-truth data obvious:
![NCLT laser scans accumulated using original ground truth](img/2012-11-16_13500_15500_gt.jpg)
<br/>*NCLT laser scans accumulated using original ground truth.*
![NCLT laser scans accumulated using refined ground truth](img/2012-11-16_13500_15500_gt_opt.jpg)
<br/>*NCLT laser scans accumulated using refined ground truth.*

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