Integration of Multi-sensor Occupancy Grids into Automotive ECUs

Tiana Rakotovao (1, 2) , Julien Mottin (1) , Diego Puschini (1) , Christian Laugier (2)

1 CEA-LETI - Commissariat à l'énergie atomique et aux énergies alternatives - Laboratoire d'Electronique et de Technologie de l'Information
2 CHROMA - Robots coopératifs et adaptés à la présence humaine en environnements dynamiques

Occupancy Grids (OGs) are a popular framework for robotic perception. They were recently adopted for performing multisensor fusion and environment mapping for autonomous vehicles. However, high computational requirements strongly hinder their integration into less powerful automotive ECUs. To overcome this problem, we propose an algorithmic improvement for mapping range measurements into OGs. Experiments were conducted on a vehicle equipped with 16 LIDAR scans. Results demonstrate that a single-core ARM cortex A9 can build now in real-time OGs that map urban traffic scenarios of 100m-by-100m.

Keywords: perception; sensor fusion; embedded integration; environment model

Domains: Computer Science [cs]; Robotics [cs.RO]

Tiana Rakotovao, Julien Mottin, Diego Puschini, Christian Laugier. Integration of Multi-sensor Occupancy Grids into Automotive ECUs. In Proceedings of the 53rd Annual Design Automation Conference (DAC 2016), Jun 2016, Austin, United States. ⟨10.1145/2897937.2898035⟩. ⟨cea-01518771⟩