Learning State-Space Models for Mapping Spatial Motion Patterns

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference article in proceedingsScientificvertaisarvioitu


Mapping the surrounding environment is essential for the successful operation of autonomous robots. While extensive research has focused on mapping geometric structures and static objects, the environment is also influenced by the movement of dynamic objects. Incorporating information about spatial motion patterns can allow mobile robots to navigate and operate successfully in populated areas. In this paper, we propose a deep state-space model that learns the map representations of spatial motion patterns and how they change over time at a certain place. To evaluate our methods, we use two different datasets: one generated dataset with specific motion patterns and another with real-world pedestrian data. We test the performance of our model by evaluating its learning ability, mapping quality, and application to downstream tasks. The results demonstrate that our model can effectively learn the corresponding motion pattern, and has the potential to be applied to robotic application tasks.
OtsikkoProceedings of the 11th European Conference on Mobile Robots, ECMR 2023
ToimittajatLino Marques, Ivan Markovic
ISBN (elektroninen)979-8-3503-0704-7
DOI - pysyväislinkit
TilaJulkaistu - 4 syysk. 2023
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaEuropean Conference on Mobile Robots - Coimbra, Portugali
Kesto: 4 syysk. 20237 syysk. 2023
Konferenssinumero: 11


NimiEuropean Conference on Mobile Robots Conference Proceedings
ISSN (elektroninen)2767-8733


ConferenceEuropean Conference on Mobile Robots


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