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arXiv · 2008.09672

Towards Autonomous Driving: a Multi-Modal 360$^{\circ}$ Perception Proposal

Jorge Beltrán·Carlos Guindel·Irene Cortés·Alejandro Barrera·Armando Astudillo·Jesús Urdiales·Mario Álvarez·Farid Bekka·Vicente Milanés·Fernando García
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Abstract

In this paper, a multi-modal 360$^{\circ}$ framework for 3D object detection and tracking for autonomous vehicles is presented. The process is divided into four main stages. First, images are fed into a CNN network to obtain instance segmentation of the surrounding road participants. Second, LiDAR-to-image association is performed for the estimated mask proposals. Then, the isolated points of every object are processed by a PointNet ensemble to compute their corresponding 3D bounding boxes and poses. Lastly, a tracking stage based on Unscented Kalman Filter is used to track the agents along time. The solution, based on a novel sensor fusion configuration, provides accurate and reliable road environment detection. A wide variety of tests of the system, deployed in an autonomous vehicle, have successfully assessed the suitability of the proposed perception stack in a real autonomous driving application.

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Jorge Beltrán, Carlos Guindel, Irene Cortés, Alejandro Barrera, Armando Astudillo, Jesús Urdiales, Mario Álvarez, Farid Bekka, Vicente Milanés, Fernando García. 2020-08-21. Towards Autonomous Driving: a Multi-Modal 360$^{\circ}$ Perception Proposal. https://arxiv.org/abs/2008.09672

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