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Poster Année : 2017

Embedded Bayesian Perception by Dynamic Occupancy Grid Filtering

Résumé

A generic Bayesian perception framework, designed to estimate a dense representation of dynamic environments, by fusing and filtering multi-sensor data, has been developed, implemented and tested on embedded devices. The main features of the approach are the followings: • Data from multiple sensors are properly fused in probabilistic occupancy grids. • Motion and robust occupancy are estimated by a specific Bayesian filter. • Short-term collision risks and object parameters are assessed. • The whole system has been implemented and tested on Nvidia embedded devices, and produces real-time results.
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Dates et versions

hal-01672134 , version 1 (23-12-2017)

Licence

Domaine public

Identifiants

  • HAL Id : hal-01672134 , version 1

Citer

Lukas Rummelhard, Christian Laugier. Embedded Bayesian Perception by Dynamic Occupancy Grid Filtering. GTC 2017 - GPU Technology Conference, May 2017, San Jose, California, United States. , pp.1, 2017. ⟨hal-01672134⟩
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