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arXiv · 2305.16702

Localization Under Consistent Assumptions Over Dynamics

Matti Pekkanen·Francesco Verdoja·Ville Kyrki
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Abstract

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.

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Matti Pekkanen, Francesco Verdoja, Ville Kyrki. 2023-05-26. Localization Under Consistent Assumptions Over Dynamics. https://doi.org/10.1109/mfi62651.2024.10705760

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