Accurate maps are a prerequisite for virtually all mobile robot tasks. Most state-of-the-art maps assume a static world; therefore, dynamic objects are filtered out of the measurements. However, this division ignores movable but non-moving---i.e., semi-static---objects, which are usually recorded in the map and treated as static objects, violating the static world assumption and causing errors in the localization. This paper presents a method for consistently modeling moving and movable objects to match the map and measurements. This reduces the error resulting from inconsistent categorization and treatment of non-static measurements. A semantic segmentation network is used to categorize the measurements into static and semi-static classes, and a background subtraction filter is used to remove dynamic measurements. Finally, we show that consistent assumptions over dynamics improve localization accuracy when compared against a state-of-the-art baseline solution using real-world data from the Oxford Radar RobotCar data set.
Citation
If you find this work useful, please consider citing:
@inproceedings{pekkanen_2024_object_oriented_mapping,
title = {Localization Under Consistent Assumptions Over Dynamics},
booktitle = {Proceedings of the IEEE International Conference on Multisensor Fusion and Integration (MFI)},
author = {Pekkanen, Matti and Verdoja, Francesco and Kyrki, Ville},
month = {Sep.},
year = {2024},
address = {Pilsen, Czechia}
}
Acknowledgements
This work was supported by Business Finland, decision 9249/31/2021. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.