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Comparative study of 3D object detection frameworks based on LiDAR data and sensor fusion techniques

  • Estimating and understanding the surroundings of the vehicle precisely forms the basic and crucial step for the autonomous vehicle. The perception system plays a significant role in providing an accurate interpretation of a vehicle's environment in real-time. Generally, the perception system involves various subsystems such as localization, obstacle (static and dynamic) detection, and avoidance, mapping systems, and others. For perceiving the environment, these vehicles will be equipped with various exteroceptive (both passive and active) sensors in particular cameras, Radars, LiDARs, and others. These systems are equipped with deep learning techniques that transform the huge amount of data from the sensors into semantic information on which the object detection and localization tasks are performed. For numerous driving tasks, to provide accurate results, the location and depth information of a particular object is necessary. 3D object detection methods, by utilizing the additional pose data from the sensors such as LiDARs, stereo cameras, provides information on the size and location of the object. Based on recent research, 3D object detection frameworks performing object detection and localization on LiDAR data and sensor fusion techniques show significant improvement in their performance. In this work, a comparative study of the effect of using LiDAR data for object detection frameworks and the performance improvement seen by using sensor fusion techniques are performed. Along with discussing various state-of-the-art methods in both the cases, performing experimental analysis, and providing future research directions.

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Metadaten
Document Type:Article
Language:English
Author:Sreenivasa Hikkal Venugopala
Parent Title (English):Journal of Physics: Conference Series
Volume:2232
Article Number:012015
Pagenumber:11
ISSN:1742-6596
URN:urn:nbn:de:hbz:1044-opus-62582
DOI:https://doi.org/10.1088/1742-6596/2232/1/012015
Publisher:IOP Publishing
Publishing Institution:Hochschule Bonn-Rhein-Sieg
Date of first publication:2022/05/12
Copyright:Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence.
Note:
2021 International Conference on Control Theory and Applications (ICoCTA 2021) November 4-6, 2021 Online
Departments, institutes and facilities:Fachbereich Informatik
Dewey Decimal Classification (DDC):0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Entry in this database:2022/06/17
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 3.0