# Localization Under Consistent Assumptions Over Dynamics

Source: [Localization Under Consistent Assumptions Over Dynamics](<https://www.sota2.com/research/paper/68fa768e1e209131cea6daf5>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:56:26.552Z

Authors: Matti Pekkanen, Francesco Verdoja, Ville Kyrki

Published: 2023-05-26T07:43:09.000Z

## 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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2305.16702>)

[PDF](<https://arxiv.org/pdf/2305.16702.pdf>)