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# Embedded Bayesian Perception by Dynamic Occupancy Grid Filtering

Lukas Rummelhard, Christian Laugier

► To cite this version:

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](https://hal-inriaHal)
# Embedded Bayesian Perception by Dynamic Occupancy Grid Filtering

Lukas Rummelhard, Christian Laugier

Chroma Research Team, Inria

## 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.

* 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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## Context and Objective

* Development of low cost sensors and miniaturized computing units enables advanced perception technologies to be embedded on realistic products.

* Most solutions based on a single-sensor approach are limited by its specific constraints, while multi-sensor fusion remains complicated.

* Object-based methods restrict the surrounding representation, and have difficulty in managing highly dynamic environments.

* **Objective**: propose a generic perception framework, especially suited to sensor data fusion and filtering at a sub-object level, offering performances adapted to the constraints of embedded systems.

## Multi-Sensor Fusion in Occupancy Grids

* The environment is represented through probabilistic occupancy grids, a dense and generic representation especially adapted to parallel computing and clear management of uncertainty

* Each sensor data is converted to occupancy estimation using specific sensor model

* Sensor occupancy estimates are combined by Bayesian fusion in every cell.

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## Temporal Filtering and Velocity Estimation

* Spatial occupancy is filtered and motion field inferred by a Bayesian filtering at the cell level (CMCDOT)

* The method combines different forms of models to optimize computations (occupancy grids for static components, particles for dynamic parts)

* Areas with contradictory data and without data are processed differently, improving both estimation precision and computation efficiency

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## Risk Estimation and Object Detection

* The occupancy and the vehicle are projected over time using the estimated dynamics, potential collisions are referenced and accumulated.

* Risk grids are thus generated, where collision risks are localized in space and time

* Dynamic parts of the scene are segmented into objects using particle propagation markers.

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## Cuda Implementation

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* Highly parallelizable framework, over cells and particles.

* Real-time implementation, optimized using Nvidia profiling utils

* Configuration with 8 Lidar layers (2x4), 1400x600 grids and 65536 velocity samples

* Jetson K1: Grid Fusion 17ms, CMCDOT 70ms

* Jetson X1: Grid Fusion 0.7ms, CMCDOT 17ms

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## Experimentations and Results

* Tests are conducted on real-time embedded perception systems, integrated on vehicles or dedicated perception units.

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## References

* A.Negre et al, "Hybrid Sampling Bayesian Occupancy Filter", IV 2014.

* L.Rummelhard et al, "Probabilistic Grid-based Collision Risk Prediction for Driving Application", ISER 2014.

* L.Rummelhard et al, "Conditional Monte Carlo Dense Occupancy Tracker", ITSC 2015.

* www.youtube.com/watch?v=TDaK3Yku8OA

* www.youtube.com/watch?v=DhKItJIhOho

* www.youtube.com/watch?v=uwIrk1TLFiM

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## Contact Information

* christian.laugier@inria.fr

* lukas.rummelhard@inria.fr