ROLL is a LiDAR-based algorithm that can provide robust and accurate localization performance against long-term scene changes.
We propose a robust LOAM-based global matching module incorporating temporary mapping, which can prevent localization failures in areas with significant scene changes or insufficient map coverings. The temporary map can be merged onto the global map once matching is reliable again.
We extend a fusion scheme to trajectories from LIO and noisy global matching. By implementing a consistency check on the derived odometry drift, we successfully prevent the optimization results from going out of bounds.
Then a rosbag named "2012-01-15_bin.bag" will be generated.
Building a map with NCLT ground truth
roslaunch roll GTmapping_nclt.launch
rosbag play <root_dir>/2012-01-15_bin.bag --clock
Localization test
By default, the algorithm will get the initial pose from topic "ground_truth". If it cannot get such a topic,
it load initial pose from variable "initialGuess".
roslaunch roll loc_nclt.launch
rosbag play <root_dir>/<another_bag> --clock
Evaluation
All evaluations were performed with matlab scripts, which are open-sourced as well
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