Embedded Bayesian Perception by Dynamic Occupancy Grid Filtering

Lukas Rummelhard 1 Christian Laugier 1
1 CHROMA - Robots coopératifs et adaptés à la présence humaine en environnements dynamiques
Inria Grenoble - Rhône-Alpes, CITI - CITI Centre of Innovation in Telecommunications and Integration of services
Abstract : 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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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, ⟨https://www.nvidia.com/en-us/gtc/⟩. ⟨hal-01672134⟩

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