A cross-prediction, hidden-state-augmented approach for Dynamic Occupancy Grid filtering - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2022

A cross-prediction, hidden-state-augmented approach for Dynamic Occupancy Grid filtering

Abstract

Accurate modeling of complex dynamic environments is a fundamental requirement in robotics and automotive applications. While grid-mapping approaches used to be limited to static settings, methods for dynamic occupancy grids have recently been developed, tracking spatial occupancy at a sub-object level, in every cell. In this paper, we present a generic dynamic occupancy grid tracker, which filters cell states and infers dynamics of the scene through the interaction of a grid-based and a particle-based model. These are set to represent different parts of the scene, and optimize particle allocation only to relevant areas, their predictions being fused accordingly. New hidden variables in the filtering process permit to address previously mishandled situations, like concurrent state predictions or specific filtering sensitivity. The presented method has been implemented, optimized on a GPU and tested on real-road conditions, embedded on an experimental vehicle.
Fichier principal
Vignette du fichier
ICARCV2022_Rummelhard.pdf (4.03 Mo) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-03974918 , version 1 (06-02-2023)

Licence

Identifiers

  • HAL Id : hal-03974918 , version 1

Cite

Lukas Rummelhard, Jean-Alix David, Andres Gonzalez Moreno, Christian Laugier. A cross-prediction, hidden-state-augmented approach for Dynamic Occupancy Grid filtering. ICARCV 2022 - 17th International Conference on Control, Automation, Robotics and Vision, Dec 2022, Singapore, Singapore. ⟨hal-03974918⟩
62 View
46 Download

Share

Gmail Mastodon Facebook X LinkedIn More