Pointcloud map preprocessing

Overview

Pointcloud map used in Autoware should be downsampled with a voxel size of around 0.1 to 0.2 meters to minimize the computational costs and memory bandwidth load. Also, you may consider using dynamic map loading with partial loading if your map is still too large to handle as a single file (~2 GB in PCD size). Please check map_loader package for more information.

At tutorial_vehicle implementation we will use the whole map, so we will downsample it with using CloudCompare.

Installing CloudCompare

You can install it by snap:

sudo snap install cloudcompare

Please check the official page for installing options.

Outlier filtering a pointcloud map

We will use SOR (Statistical Outlier Filter) for our mapping process. Statistical Outlier Removal (SOR) is a method for detecting and removing anomalous data (points far from their neighbors) points from a data set. It works by computing the mean and standard deviation of the data points, and then identifying any points that fall outside a certain number of standard deviations from the mean. These points are then flagged as outliers and removed from the pointcloud.

1. Please open CloudCompare and drag your point cloud here. Then, you can select your point cloud map by clicking on it in the DB tree panel or on the map.
2. Then you can click SOR filter button on the top panel.

CloudCompare

• The SOR parameter filters panel will appear. You can change these parameters according to your map specifications. Please refer to the documentation for more information about these parameters. We will use the default parameters for our documentation.

SOR Filter parameters

• After the filtering process is finished, you should select pointcloud on the DB Tree panel as well.

Select your cleaned pointcloud

Now, you can save your downsampled pointcloud with ctrl + s or you can click save button from File bar. Then, this pointcloud can be used by autoware.

RAW Pointcloud Map	SOR Filtered Pointcloud Map

Downsampling a pointcloud map

There are three subsampling methods on CloudCompare, we are using Space method for subsampling, but you can use other methods if you want.

1. Please open CloudCompare and drag your point cloud here. Then, you can select your point cloud map by clicking on it in the DB tree panel or on the map.
2. Then you can click subsample button on the top panel.

CloudCompare

1. Please select on your subsample method, we will use space for tutorial_vehicle.
2. Then you can select options. For example, we need to determine minimum space between points. (Please be careful in this section, subsampling is depending on your map size, computer performance, etc.) We will set this value 0.2 for tutorial_vehicle's map.

Pointcloud subsampling

• After the subsampling process is finished, you should select pointcloud on the DB Tree panel as well.

Select your downsampled pointcloud

Now, you can save your downsampled pointcloud with ctrl + s or you can click save button from File bar. Then, this pointcloud can be used by autoware.

RAW Pointcloud Map	(0.2 Space) Sub-sampled Pointcloud Map

Dynamic object removal

The dynamic object removal package, provided by the Localization & Mapping WG, removes dynamic objects from a point cloud using the output of the lidar-centerpoint package. Please refer to the package documentation for more information. Also, if you want to learn how to use this package with the LIO-SAM mapping method, you can follow this documentation: Create LIO-SAM map with object removal.