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Article

Real-Time 3D Object Detection and SLAM Fusion in a Low-Cost LiDAR Test Vehicle Setup

1
Algoritmi Centre, University of Minho, 4800-058 Guimarães, Portugal
2
Bosch Company, 4700-113 Braga, Portugal
3
Computer Graphics Center, University of Minho, 4800-058 Guimarães, Portugal
4
Department of Engineering, University of Trás-os-Montes and Alto Douro, 5000-801 Vila Real, Portugal
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Paolo Spagnolo
Sensors 2021, 21(24), 8381; https://doi.org/10.3390/s21248381
Received: 31 October 2021 / Revised: 3 December 2021 / Accepted: 7 December 2021 / Published: 15 December 2021
(This article belongs to the Special Issue Sensors for Object Detection, Classification and Tracking)
Recently released research about deep learning applications related to perception for autonomous driving focuses heavily on the usage of LiDAR point cloud data as input for the neural networks, highlighting the importance of LiDAR technology in the field of Autonomous Driving (AD). In this sense, a great percentage of the vehicle platforms used to create the datasets released for the development of these neural networks, as well as some AD commercial solutions available on the market, heavily invest in an array of sensors, including a large number of sensors as well as several sensor modalities. However, these costs create a barrier to entry for low-cost solutions for the performance of critical perception tasks such as Object Detection and SLAM. This paper explores current vehicle platforms and proposes a low-cost, LiDAR-based test vehicle platform capable of running critical perception tasks (Object Detection and SLAM) in real time. Additionally, we propose the creation of a deep learning-based inference model for Object Detection deployed in a resource-constrained device, as well as a graph-based SLAM implementation, providing important considerations, explored while taking into account the real-time processing requirement and presenting relevant results demonstrating the usability of the developed work in the context of the proposed low-cost platform. View Full-Text
Keywords: autonomous driving; deep learning methods; LiDAR scanners; 3D object detection; onboard inference; SLAM; vehicles setup autonomous driving; deep learning methods; LiDAR scanners; 3D object detection; onboard inference; SLAM; vehicles setup
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MDPI and ACS Style

Fernandes, D.; Afonso, T.; Girão, P.; Gonzalez, D.; Silva, A.; Névoa, R.; Novais, P.; Monteiro, J.; Melo-Pinto, P. Real-Time 3D Object Detection and SLAM Fusion in a Low-Cost LiDAR Test Vehicle Setup. Sensors 2021, 21, 8381. https://doi.org/10.3390/s21248381

AMA Style

Fernandes D, Afonso T, Girão P, Gonzalez D, Silva A, Névoa R, Novais P, Monteiro J, Melo-Pinto P. Real-Time 3D Object Detection and SLAM Fusion in a Low-Cost LiDAR Test Vehicle Setup. Sensors. 2021; 21(24):8381. https://doi.org/10.3390/s21248381

Chicago/Turabian Style

Fernandes, Duarte, Tiago Afonso, Pedro Girão, Dibet Gonzalez, António Silva, Rafael Névoa, Paulo Novais, João Monteiro, and Pedro Melo-Pinto. 2021. "Real-Time 3D Object Detection and SLAM Fusion in a Low-Cost LiDAR Test Vehicle Setup" Sensors 21, no. 24: 8381. https://doi.org/10.3390/s21248381

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