In this paper, a multi-modal 360∘ 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.
@article{arxiv.2008.09672,
title = {Towards Autonomous Driving: a Multi-Modal 360$^{\circ}$ Perception Proposal},
author = {Jorge Beltrán and Carlos Guindel and Irene Cortés and Alejandro Barrera and Armando Astudillo and Jesús Urdiales and Mario Álvarez and Farid Bekka and Vicente Milanés and Fernando García},
journal= {arXiv preprint arXiv:2008.09672},
year = {2020}
}