Closed nahueespinosa closed 6 months ago
Public datasets rarely come with a map, so we will need to create it from the data. LAMBKIN is already capable of running SLAM with the following datasets:
@nahueespinosa @serraramiro1 FYI rosbags-convert
can be used to migrate datasets from ROS 1 bag format to ROS 2 bag format.
Yes! I've tried it, and aside from having issues with latched topics, for instance /tf_static
, it works really well.
I had the same output with this python package https://gitlab.com/ternaris/rosbags which is way easier to install as it doesn't require ROS.
I've decided to start with the magazino
datasets. Reason being:
They're short, and manageable from a provisioning POV.
git-lfs
has a strong limit at 5GB
https://docs.github.com/en/enterprise-server@3.5/repositories/working-with-files/managing-large-files/about-git-large-file-storage#about-git-large-file-storage
, so I'll go with those and then carefully upload some others.
I think we can call this done, as generic as the description is.
Description
As part of #163, we need to define which input datasets to use for benchmarking. We should aim for selecting a subset of the available datasets that represent different localization conditions. The nature of those conditions remains to be defined, but in principle:
Regarding sensor configuration, we currently support 2D LIDAR + odometry.
The common format will most likely need to be a ROS 2 bag, so some preprocessing scripts will be needed.
Definition of done
Additional considerations
We have collected public datasets in the past (although the pre-processing was done with ROS 1 in mind), see https://github.com/ekumenlabs/RnD/issues/5.
We should also check out new collections, for example: https://github.com/mint-lab/awesome-robotics-datasets.