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BEV-Net: A Bird's Eye View Object Detection Network for LiDAR Point Cloud

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

LiDAR-only object detection is essential for autonomous driving systems and is a challenging problem. For the representation of a bird's eye view LiDAR point-cloud, this paper proposes a single-stage object detector. The detector can output classification information and accurate positioning information for multi-category objects. In this paper, the detector's design methods are detailed from a bird's eye view LiDAR point-cloud encoding, network design, data augmentation, etc. The detector was evaluated on three challenging datasets: KITTI, nuScenes and Waymo. The experimental results demonstrated that the proposed detector can accurately achieve object detection tasks and the detection speed can reach 26.9 FPS. Both the precision and the speed can meet the requirements of most autonomous driving scenarios.

Original languageEnglish
Title of host publication2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5973-5980
Number of pages8
ISBN (Electronic)9781665417143
DOIs
StatePublished - 2021
Event2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021 - Prague, Czech Republic
Duration: 27 Sep 20211 Oct 2021

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Conference

Conference2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021
Country/TerritoryCzech Republic
CityPrague
Period27/09/211/10/21

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